Tesi sul tema "Textured images"
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1
Li, Zhongqiang. "Segmentation of textured images". Thesis, University of Central Lancashire, 1991. http://clok.uclan.ac.uk/20270/.
Abstract (sommario):
This study is dedicated to the problem of segmenting monochrome images into distinct homogeneous regions by texture properties. The principle of the approaches to texture segmentation adopted in this thesis is mapping a textured image into a grey level image so that conventional segmentation techniques by intensity can be applied. Three novel approaches to texture segmentation have been developed in this thesis. They are called the Local Feature Statistics Approach (LFS), the Local Spectral Mapping Approach (LSM) and the Multichannel Spatial Filtering Approach (MSF). In the LFS approach, a multiresolution scheme for extracting texture features is introduced. This scheme produces features which can describe texture characteristics at different resolution levels. The gradient vector at each resolution level is used as the local texture feature. Based on the population statistics of gradient magnitude and direction in a local observation window, two novel texture measures, named as the Linear Gradient Magnitude Enhancement Measure (LGME) and the Linear Gradient Direction Enhancement Measure (LGDE), are developed to enhance different texture characteristics. In the LSM approach, the new scheme for the extraction of local texture features is based on performing transformations on the power spectra of local regions. The power spectrum of a local region is divided into a number of rings or wedges, and local spectral vectors are formed by summing the energy in these rings or wedges as vector elements. Two new texture measures, named as the Linear Radial Feature Enhancement Measure (LRFE) and the Linear Angular Feature Enhancement Measure (LAFE), are developed to highlight different texture characteristics. The MSF approach is based on the Multichannel Spatial Filtering Model (MSFM) for the human visual cortex. It is assumed in this approach that a texture can be characterised by its principal spatial frequency components, and that these components can be captured by a number of narrowband spatial filters. A new class of filters, called the Gaussian-Smoothed Fan (GSF) filters, is developed to perform channel filtering operations. The passband characteristic of these GSF filters is flatter than that of the Gabor filters, thus their bandwidths are inherently better defined. Computational algorithms based on these three new approaches are implemented and applied to a set of textured images. Good segmentation results are obtained, with more than 92% of the pixel population of each of the test images (derived from Brodatzs texture album) being correctly classified by all the three approaches. By comparison, the newly-developed GSF filters used in the MSF approach have an important advantage over the Gabor filters in that they can produce better defined boundaries between texture regions.
2
Leng, Xiaoling. "Analysis of some textured images by transputer". Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324405.
3
Noriega, Leonardo Antonio. "The colorimetric segmentation of textured digital images". Thesis, Southampton Solent University, 1998. http://ssudl.solent.ac.uk/2444/.
Abstract (sommario):
This study approaches the problem of colour image segmentation as a pattern recognition task. This leads to the problem being broken down into two component parts: feature extraction and classification algorithms. Measures to enable the objective assessment of segmentation algorithms are considered. In keeping with this pattern-recognition based philosophy, the issue of texture is approached by a consideration of features, follwed by experimentation based on classification. Techniques based on Gabor filters and fractal dimension are compared. Also colour is considered in terms of its features, and a systematic exploration of colour features in undertaken. The technique for assessing colour features is also used as the basis for a segmentation algorithm that can be used for combining colour and texture. In this study, several novel techniques are presented and discussed. Firstly a methodology for the judgement of image segmentation algorithms. Secondly a technique for segmenting images using fractal dimension is presented, including a novel application of information dimension. thirdly an objective assessment of colour spaces using the techniques discussed as the first point of this study. Finally strategies for combining colour and texture in the segmentation process are discussed and techniques presented.
4
Bradbury, Teresa Ann. "Textured imprints, images, social change, and cultural memory". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29144.pdf.
5
Williams, Ian Anthony. "Edge detection of textured images using multiple scales and statistics". Thesis, Manchester Metropolitan University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491176.
Abstract (sommario):
Texture is often the discriminator for different regions of an image. It can allow a region, or an object's edges to be represented as a difference in the pixel texture properties, as opposed to a difference in intensity. When analysing images with significant levels of noise, clutter or texture, the inadequacies of many common edge detectors has been noted. Where these traditional techniques fail, texture based edge detection proves more appropriate. In this work novel statistical edge detectors particularly suited for textured images are designed, presented and analysed. These are based on two-sample statistical tests which are used to evaluate any local image texture differences and by applying a pixel region mask to the image analyse the statistical properties of that region. The technique is enhanced further by combining multiple sized masks and multiple-statistical tests using a neural network traineq to classify many edge types using outputs from this technique. This results in a more robust and consistent detection of texture edge profiles. An analysis of these novel techniques shows an improved performance over the current standards in edge detection, namely, the benchmark Canny filter. This work further investigates the inadequacies of current edge detector evaluation metrics, and as a contribution to this field presents a novel grey-scale comparison metric for objectively evaluating edge detection performance.
6
Văcar, Cornelia Paula. "Inversion for textured images : unsupervised myopic deconvolution, model selection, deconvolution-segmentation". Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0131/document.
Abstract (sommario):
Ce travail est dédié à la résolution de plusieurs problèmes de grand intérêt en traitement d’images : segmentation, choix de modèle et estimation de paramètres, pour le cas spécifique d’images texturées indirectement observées (convoluées et bruitées). Dans ce contexte, les contributions de cette thèse portent sur trois plans différents : modéle, méthode et algorithmique.Du point de vue modélisation de la texture, un nouveaumodèle non-gaussien est proposé. Ce modèle est défini dans le domaine de Fourier et consiste en un mélange de Gaussiennes avec une Densité Spectrale de Puissance paramétrique.Du point de vueméthodologique, la contribution est triple –troisméthodes Bayésiennes pour résoudre de manière :–optimale–non-supervisée–des problèmes inverses en imagerie dans le contexte d’images texturées ndirectement observées, problèmes pas abordés dans la littérature jusqu’à présent.Plus spécifiquement,1. la première méthode réalise la déconvolution myope non-supervisée et l’estimation des paramètres de la texture,2. la deuxième méthode est dédiée à la déconvolution non-supervisée, le choix de modèle et l’estimation des paramètres de la texture et, finalement,3. la troisième méthode déconvolue et segmente une image composée de plusieurs régions texturées, en estimant au même temps les hyperparamètres (niveau du signal et niveau du bruit) et les paramètres de chaque texture.La contribution sur le plan algorithmique est représentée par une nouvelle version rapide de l’algorithme Metropolis-Hastings. Cet algorithme est basé sur une loi de proposition directionnelle contenant le terme de la ”direction de Newton”. Ce terme permet une exploration rapide et efficace de l’espace des paramètres et, de ce fait, accélère la convergenceThis thesis is addressing a series of inverse problems of major importance in the fieldof image processing (image segmentation, model choice, parameter estimation, deconvolution)in the context of textured images. In all of the aforementioned problems theobservations are indirect, i.e., the textured images are affected by a blur and by noise. Thecontributions of this work belong to three main classes: modeling, methodological andalgorithmic. From the modeling standpoint, the contribution consists in the development of a newnon-Gaussian model for textures. The Fourier coefficients of the textured images are modeledby a Scale Mixture of Gaussians Random Field. The Power Spectral Density of thetexture has a parametric form, driven by a set of parameters that encode the texture characteristics.The methodological contribution is threefold and consists in solving three image processingproblems that have not been tackled so far in the context of indirect observationsof textured images. All the proposed methods are Bayesian and are based on the exploitingthe information encoded in the a posteriori law. The first method that is proposed is devotedto the myopic deconvolution of a textured image and the estimation of its parameters.The second method achieves joint model selection and model parameters estimation froman indirect observation of a textured image. Finally, the third method addresses the problemof joint deconvolution and segmentation of an image composed of several texturedregions, while estimating at the same time the parameters of each constituent texture.Last, but not least, the algorithmic contribution is represented by the development ofa new efficient version of the Metropolis Hastings algorithm, with a directional componentof the proposal function based on the”Newton direction” and the Fisher informationmatrix. This particular directional component allows for an efficient exploration of theparameter space and, consequently, increases the convergence speed of the algorithm.To summarize, this work presents a series of methods to solve three image processingproblems in the context of blurry and noisy textured images. Moreover, we present twoconnected contributions, one regarding the texture models andone meant to enhance theperformances of the samplers employed for all of the three methods
7
Meléndez, Rodríguez Jaime Christian. "Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification". Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/8487.
Abstract (sommario):
This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes.
8
Dura, Martinez Esther. "Reconstruction and classification of man-made objects and textured seafloors from side-scan sonar images". Thesis, Heriot-Watt University, 2002. http://hdl.handle.net/10399/409.
9
Achddou, Raphaël. "Synthetic learning for neural image restoration methods". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT006.
Abstract (sommario):
La photographie occupe aujourd'hui une place prépondérante dans nos vies. De plus, les attentes en terme de qualité des images augmentent tandis que la taille des appareils imageurs diminuent. Dans ce contexte, l'amélioration des algorithmes de traitement d'image est primordial.Dans ce manuscrit, on s'intéresse particulièrement aux tâches de restauration des images. Le but est de produire une image propre à partir d'une ou plusieurs observations bruitées de la même scène. Pour ces problèmes, les méthodes d'apprentissage profond ont connu un essor spectaculaire dans la dernière décennie, surpassant l'état de l'art pour la grande majorité des tests traditionnels.Bien que ces méthodes produisent des résultats impressionnants, elles présentent un certain nombre d'inconvénients. Tout d'abord, elles sont difficilement interprétables de part leur fonctionnement “boite noire”. De plus, elles généralisent assez mal à des modalités d'acquisition ou de distorsion absentes de la base de donnée d'apprentissage. Enfin, elles nécessitent des bases de données volumineuses, qui sont parfois difficile à acquérir.On se propose d'attaquer ces différents problèmes en remplaçant l'acquisition des données par un algorithme simple de génération de d'image, basé sur le modèle feuilles mortes. Bien que ce modèle soit très simple, les images générées ont des propriétés statistiques proches de celles des images naturelles et de nombreuses propriétés d'invariances (échelle, translation, rotation, contraste…). Entraîner un réseau de restauration avec ce genre d'image nous permet d'identifier les propriétés importantes des images pour la réussite des réseaux de restauration. De plus, cette méthode permet de s'affranchir de l'acquisition des données, qui peut s'avérer fastidieuse.Après avoir présenté ce modèle, on montre dans un premier temps que la méthode proposée permet d'obtenir des performances de restauration très proches des méthodes traditionnelles pour des tâches relativement simples. Après quelques adaptations du modèle, l'apprentissage synthétique permet aussi de s'attaquer à des problèmes concrets difficiles, comme le débruitage d'images RAW. On propose ensuite une étude statistique de distribution des couleurs des images naturelles, permettant d'élaborer un modèle parametrique réaliste d'échantillonnage des couleurs pour notre algorithme de génération. Enfin, on présente une nouvelle fonction de perte perceptuelle basée sur les protocoles d'évaluation des cameras, faisant intervenir les images feuilles mortes. Les entrainement réalisés avec cette fonction montre qu'on peut conjointement optimiser l'évaluation des appareils, tout en conservant des performances identiques sur les images naturellesPhotography has become an important part of our lives. In addition, expectations in terms of image quality are increasing while the size of imaging devices is decreasing. In this context, the improvement of image processing algorithms is essential.In this manuscript, we are particularly interested in image restoration tasks. The goal is to produce a clean image from one or more noisy observations of the same scene. For these problems, deep learning methods have grown dramatically in the last decade, outperforming the state of the art for the vast majority of traditional tests.While these methods produce impressive results, they have a number of drawbacks. First of all, they are difficult to interpret because of their "black box" operation. Moreover, they generalize rather poorly to acquisition or distortion modalities absent from the training database. Finally, they require large databases, which are sometimes difficult to acquire.We propose to attack these different problems by replacing the data acquisition by a simple image generation algorithm, based on the dead leaves model. Although this model is very simple, the generated images have statistical properties close to those of natural images and many invariance properties (scale, translation, rotation, contrast...). Training a restoration network with this kind of image allows us to identify the important properties of the images for the success of the restoration networks. Moreover, this method allows us to get rid of the data acquisition, which can be tedious.After presenting this model, we show that the proposed method allows to obtain restoration performances very close to traditional methods for relatively simple tasks. After some adaptations of the model, synthetic learning also allows us to tackle difficult concrete problems, such as RAW image denoising. We then propose a statistical study of the color distribution of natural images, allowing to elaborate a realistic parametric model of color sampling for our generation algorithm. Finally, we present a new perceptual loss function based on camera evaluation protocols, using the dead leaf images. The training performed with this function shows that we can jointly optimize the evaluation of the cameras, while keeping identical performances on natural images
10
Casaca, Wallace Correa de Oliveira [UNESP]. "Restauração de imagens digitais com texturas utilizando técnicas de decomposição e equações diferenciais parciais". Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/94247.
Abstract (sommario):
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Neste trabalho propomos quatro novas abordagens para tratar o problema de restauração de imagens reais contendo texturas sob a perspectiva dos temas: reconstrução de regiões danificadas, remoção de objetos, e eliminação de ruídos. As duas primeiras abor dagens são designadas para recompor partes perdias ou remover objetos de uma imagem real a partir de formulações envolvendo decomposiçãode imagens e inpainting por exem- plar, enquanto que as duas últimas são empregadas para remover ruído, cujas formulações são baseadas em decomposição de três termos e equações diferenciais parciais não lineares. Resultados experimentais atestam a boa performace dos protótipos apresentados quando comparados à modelagens correlatas da literatura.
In this paper we propose four new approaches to address the problem of restoration of real images containing textures from the perspective of reconstruction of damaged areas, object removal, and denoising topics. The first two approaches are designed to reconstruct missing parts or to remove objects of a real image using formulations based on image de composition and exemplar based inpainting, while the last two other approaches are used to remove noise, whose formulations are based on decomposition of three terms and non- linear partial di®erential equations. Experimental results attest to the good performance of the presented prototypes when compared to modeling related in literature.
11
Park, Keith Marron. "The global-to-local search method: A systematic search procedure that uses the context of the textured layout to locate and detect low-contrast targets in aerial images". CSUSB ScholarWorks, 1993. https://scholarworks.lib.csusb.edu/etd-project/700.
12
Siqueira, Fernando Roberti de 1989. "Multi-scale approaches to texture description = Abordagens multiescala para descrição de textura". [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275604.
Abstract (sommario):
Orientadores: Hélio Pedrini, William Robson SchwartzDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: Visão computacional e processamento de imagens desempenham um papel importante em diversas áreas, incluindo detecção de objetos e classificação de imagens, tarefas muito importantes para aplicações em imagens médicas, sensoriamento remoto, análise forense, detecção de pele, entre outras. Estas tarefas dependem fortemente de informação visual extraída de imagens que possa ser utilizada para descrevê-las eficientemente. Textura é uma das principais propriedades usadas para descrever informação tal como distribuição espacial, brilho e arranjos estruturais de superfícies. Para reconhecimento e classificação de imagens, um grande grupo de descritores de textura foi investigado neste trabalho, sendo que apenas parte deles é realmente multiescala. Matrizes de coocorrência em níveis de cinza (GLCM) são amplamente utilizadas na literatura e bem conhecidas como um descritor de textura efetivo. No entanto, este descritor apenas discrimina informação em uma única escala, isto é, a imagem original. Escalas podem oferecer informações importantes em análise de imagens, pois textura pode ser percebida por meio de diferentes padrões em diferentes escalas. Dessa forma, duas estratégias diferentes para estender a matriz de coocorrência para múltiplas escalas são apresentadas: (i) uma representação de escala-espaço Gaussiana, construída pela suavização da imagem por um filtro passa-baixa e (ii) uma pirâmide de imagens, que é definida pelo amostragem de imagens em espaço e escala. Este descritor de textura é comparado com outros descritores em diferentes bases de dados. O descritor de textura proposto e então aplicado em um contexto de detecção de pele, como forma de melhorar a acurácia do processo de detecção. Resultados experimentais demonstram que a extensão multiescala da matriz de coocorrência exibe melhora considerável nas bases de dados testadas, exibindo resultados superiores em relação a diversos outros descritores, incluindo a versão original da matriz de coocorrência em escala única
Abstract: Computer vision and image processing techniques play an important role in several fields, including object detection and image classification, which are very important tasks with applications in medical imagery, remote sensing, forensic analysis, skin detection, among others. These tasks strongly depend on visual information extracted from images that can be used to describe them efficiently. Texture is one of the main used characteristics that describes information such as spatial distribution, brightness and surface structural arrangements. For image recognition and classification, a large set of texture descriptors was investigated in this work, such that only a small fraction is actually multi-scale. Gray level co-occurrence matrices (GLCM) have been widely used in the literature and are known to be an effective texture descriptor. However, such descriptor only discriminates information on a unique scale, that is, the original image. Scales can offer important information in image analysis, since texture can be perceived as different patterns at distinct scales. For that matter, two different strategies for extending the GLCM to multiple scales are presented: (i) a Gaussian scale-space representation, constructed by smoothing the image with a low-pass filter and (ii) an image pyramid, which is defined by sampling the image both in space and scale. This texture descriptor is evaluated against others in different data sets. Then, the proposed texture descriptor is applied in skin detection context, as a mean of improving the accuracy of the detection process. Experimental results demonstrated that the GLCM multi-scale extension has remarkable improvements on tested data sets, outperforming many other feature descriptors, including the original GLCM
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
13
Casaca, Wallace Correa de Oliveira. "Restauração de imagens digitais com texturas utilizando técnicas de decomposição e equações diferenciais parciais /". São José do Rio Preto : [s.n.], 2010. http://hdl.handle.net/11449/94247.
Abstract (sommario):
Orientador: Maurílio BoaventuraBanca: Evanildo Castro Silva Júnior
Banca: Alagacone Sri Ranga
Resumo: Neste trabalho propomos quatro novas abordagens para tratar o problema de restauração de imagens reais contendo texturas sob a perspectiva dos temas: reconstrução de regiões danificadas, remoção de objetos, e eliminação de ruídos. As duas primeiras abor dagens são designadas para recompor partes perdias ou remover objetos de uma imagem real a partir de formulações envolvendo decomposiçãode imagens e inpainting por exem- plar, enquanto que as duas últimas são empregadas para remover ruído, cujas formulações são baseadas em decomposição de três termos e equações diferenciais parciais não lineares. Resultados experimentais atestam a boa performace dos protótipos apresentados quando comparados à modelagens correlatas da literatura.
Abstract: In this paper we propose four new approaches to address the problem of restoration of real images containing textures from the perspective of reconstruction of damaged areas, object removal, and denoising topics. The first two approaches are designed to reconstruct missing parts or to remove objects of a real image using formulations based on image de composition and exemplar based inpainting, while the last two other approaches are used to remove noise, whose formulations are based on decomposition of three terms and non- linear partial di®erential equations. Experimental results attest to the good performance of the presented prototypes when compared to modeling related in literature.
Mestre
14
Negri, Tamiris Trevisan. "Descritores locais de textura para classificação de imagens coloridas sob variação de iluminação". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-02032018-112555/.
Abstract (sommario):
A classificação de texturas coloridas sob diferentes condições de iluminação é um desafio na área de visão computacional, e depende da eficiência dos descritores de textura em capturar características que sejam discriminantes independentemente das propriedades da fonte de luz incidente sobre o objeto. Visando melhorar o processo de classificação de texturas coloridas iluminadas com diferentes fontes de luz, este trabalho propõe três novos descritores, nomeados Opponent Color Local Mapped Pattern (OCLMP), que combina o descritor de texturas por padrões locais mapeados (Local Mapped Pattern - LMP) com a teoria de cores oponentes; Color Intensity Local Mapped Pattern (CILMP), que extrai as informações de cor e textura de maneira integrada, levando em consideração a textura da cor, combinando estas informações com características da luminância da textura em uma análise multiresolução; e Extended Color Local Mapped Pattern (ECLMP), que utiliza dois operadores para extrair informações de cor e textura de forma integrada (textura da cor) combinadas com informações apenas de textura (sem cor) de uma imagem. Todos esses novos descritores propostos são paramétricos e, sendo o ajuste ótimo de seus parâmetros não trivial, o processo exige um tempo excessivo de computação. Portanto, foi proposto nesta tese a utilização de algoritmos genéticos para o ajuste automático dos parâmetros. A avaliação dos descritores propostos foi realizada em duas bases de dados de texturas coloridas com variação de iluminação: RawFooT (Raw Food Texture Database) e KTH-TIPS- 2b (Textures under varying Illumination, Pose and Scale Database), utilizando-se um classificador. Os resultados experimentais mostraram que os descritores propostos são mais robustos à variação de iluminação do que outros decritores de textura comumente utilizados na literatura. Os descritores propostos apresentaram um desempenho superior aos descritores comparados em 15% na base de dados RawFooT e 4% na base de dados KTH-TIPS-2b.Color texture classification under varying illumination remains a challenge in the computer vision field, and it greatly relies on the efficiency at which the texture descriptors capture discriminant features, independent of the illumination condition. The aim of this thesis is to improve the classification of color texture acquired with varying illumination sources. We propose three new color texture descriptors, namely: the Opponent Color Local Mapped Pattern (OCLMP), which combines a local methodology (LMP) with the opponent colors theory, the Color Intensity Local Mapped Pattern (CILMP), which extracts color and texture information jointly, in a multi-resolution fashion, and the Extended Color Local Mapped Pattern (ECLMP), which applies two operators to extract color and texture information jointly as well. As the proposed methods are based on the LMP algorithm, they are parametric functions. Finding the optimal set of parameters for the descriptor can be a cumbersome task. Therefore, this work proposes the use of genetic algorithms to automatically adjust the parameters. The methods were assessed using two data sets of textures acquired using varying illumination sources: the RawFooT (Raw Food Texture Database), and the KTH-TIPS-2b (Textures under varying Illumination, Pose and Scale Database). The experimental results show that the proposed descriptors are more robust to variations to the illumination source than other methods found in the literature. The improvement on the accuracy was higher than 15% on the RawFoot data set, and higher than 4% on the KTH-TIPS-2b data set.
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Pasáček, Václav. "Segmentace obrazu podle textury". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236463.
Abstract (sommario):
Image segmentation is an important step in image processing. A traditional way how to segment an image is a texture-based segmentation that uses texture features to describe image texture. In this work, Local Binary Patterns (LBP) are used for image texture representation. Texture feature is a histogram of occurences of LBP codes in a small image window. The work also aims to comparison of results of various modifications of Local Binary Patterns and their usability in the image segmentation which is done by unsupervised clustering of texture features. The Fuzzy C-Means algorithm is finally used for the clustering in this work.
16
Leite, Tatiane Silvia. "Melhoria da atratividade de faces em imagens = Enhancement of faces attractiveness in images". [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259371.
Abstract (sommario):
Orientador: José Mario De MartinoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: O rosto desempenha um papel importante na comunicação e expressão de emoções. Por ser o cartão de visitas individual e caracterizar a primeira impressão de cada um, sua aparência e seu formato tornam-se alvo de diversos estudos. Um rosto mais atraente é capaz de capturar com maior facilidade não apenas a atenção de quem o observa, como também sua empatia. Nesta linha, o presente trabalho tem como objetivo o desenvolvimento de uma metodologia para manipulação e transformação de imagens fotográficas de faces com a finalidade de aumentar a atratividade destes rostos. Para isso, foram abordados dois aspectos de modificação da face: o geométrico e o de textura da pele do rosto. No contexto deste trabalho, foi construída uma base de imagens de faces. Nas imagens desta base foram identificados pontos de interesse e calculadas distâncias entre eles para a caracterização das proporções da face. Adicionalmente, foi atribuído um grau de atratividade para cada face, a partir de avaliação realizada por um grupo de 40 voluntários. As medidas de proporção e atratividade foram utilizadas, no processo de melhoria geométrica da face, como conjunto de treinamento para os algoritmos de aprendizado de máquina. Como resultado do processamento são geradas novas medidas para o rosto que se deseja tornar mais atraente. Utilizando a técnica de warping, a imagem do rosto de entrada é modificada para as novas medidas encontradas. A imagem resultante deste processo serve como imagem de entrada para o processo de modificação da textura. Neste processamento é gerada uma nova imagem com a cor dos pixels da região de pele do rosto alterada. A principal contribuição deste trabalho consiste em unir o processo de modificação geométrica do rosto à modificação de textura da pele. Esta união resultou em um ganho de atratividade maior do que se estas técnicas fossem utilizadas separadamente. Este ganho foi comprovado com testes de pós-avaliação realizados com voluntários analisando os resultados finais nas imagens
Abstract: The face plays an important role in communication and expression of emotions. Face characterizes the first impression of each person; thus, its appearance and shape became the target of several studies. An attractive face is capable of capturing more easily not only the attention of the beholder, as well as his/her empathy. In this vein, this study aims to develop a methodology for handling and processing of images of faces in order to increase the attractiveness of these faces. It was addressed two aspects of modification of the face: the geometric and texture (considering only the skin of the face). In this work, a large database of face images was built. All these faces were marked with feature points and from them it was taken measures considered interesting to analyze the dimensions and proportions of the faces. Besides that, they were also evaluated according to their degree of attraction by a group of volunteers. This information was used in the enhancement of the face geometry, using machine learning algorithms. At this stage new measures were generated for the input face which is considered in the beautification process. Using the technique of warping, the input face image is warped to fit the new measures found by the algorithms. The resulting image from this process serves as the input image to the process of texture modification. At this stage it is generated a new image with the color of pixels in the region of skin of the face changed. The main contribution of this work is to join the process of face geometry modification with the process of face skin texture modification. The result of this union generates image faces which have greater enhancement of attractiveness than if the processes were used separately. This gain was confirmed by post-evaluation tests conducted with volunteers that analyzed the final results
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
17
Kim, Kyu-Heon. "Segmentation of natural texture images using a robust stochastic image model". Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307927.
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Azzabou, Noura. "Variable Bandwidth Image Models for Texture-Preserving Enhancement of Natural Images". Paris Est, 2008. http://pastel.paristech.org/4041/01/ThesisNouraAzzabou.pdf.
Abstract (sommario):
Cette thèse s’intéresse aux problèmes de restauration d’images et de préservation de textures. Cette tâche nécessite un modèle image qui permet de caractériser le signal qu’on doit obtenir. Un tel model s’appuie sur la définition de l’interaction entre les pixels et qui est caractérisé par deux aspects : (1) la similarité photométrique entre les pixels (2) la distance spatiale entre les pixels qui peut être comparée à une grandeur d’échelle. La première partie de la thèse introduit un nouveau modèle non paramétrique d’image. Ce modèle permet d’obtenir une description adaptative de l’image en utilisant des noyaux de taille variable obtenue à partir d’une étape de classification effectuée au préalable. La deuxième partie introduit une autre approche pour décrire la dépendance entre pixels d’un point de vue géométrique. Ceci est effectué à l’aide d’un modèle statistique de la co-occurrence entre les observations de point de vue géométrique. La dernière partie est une nouvelle technique de sélection automatique (pour chaque pixel) de la taille des noyaux utilisé au cours du filtrage. Cette thèse est conclue avec l’application de cette dernière approche dans différents contextes de filtrage ce qui montre sa flexibilité vis-à-vis des contraintes liées aux divers problèmes traitésThis thesis is devoted to image enhancement and texture preservation issues. This task involves an image model that describes the characteristics of the recovered signal. Such a model is based on the definition of the pixels interaction that is often characterized by two aspects (i) the photometric similarity between pixels (ii) the spatial distance between them that can be compared to a given scale. The first part of the thesis, introduces novel non-parametric image models towards more appropriate and adaptive image description using variable bandwidth approximations driven from a soft classification in the image. The second part introduces alternative means to model observations dependencies from geometric point of view. This is done through statistical modeling of co-occurrence between observations and the use of multiple hypotheses testing and particle filters. The last part is devoted to novel adaptive means for spatial bandwidth selection and more efficient tools to capture photometric relationships between observations. The thesis concludes with providing other application fields of the last technique towards proving its flexibility toward various problem requirements
19
Glotfelty, Joseph Edmund. "Automatic selection of optimal window size and shape for texture analysis". Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=898.
Abstract (sommario):
Thesis (M.A.)--West Virginia University, 1999.Title from document title page. Document formatted into pages; contains vii, 59 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 55-59).
20
Green, Lori Anne. "Tiled texture synthesis". Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/429.
Abstract (sommario):
In this thesis a new image-based texturing method has been developed. This new method allows users to synthesize tiled textures that can be mapped to any quadrilateral mesh without discontinuity or singularity. An interface has been developed that allows user control over out put textures. Three methods have been included in the interface to create a periodic looking texture for 3D models and two methods have been developed to create wallpaper images (repeating textures on a 2D surface). Using these texturing methods, texturing problems are simplified, and more time can be spent solving artistic problems.
21
Lladó, Bardera Xavier. "Texture recognition under varying imaging geometries". Doctoral thesis, Universitat de Girona, 2004. http://hdl.handle.net/10803/7721.
Abstract (sommario):
La visió és probablement el nostre sentit més dominant a partir del qual derivem la majoria d'informació del món que ens envolta. A través de la visió podem percebre com són les coses, on són i com es mouen. En les imatges que percebem amb el nostre sistema de visió podem extreure'n característiques com el color, la textura i la forma, i gràcies a aquesta informació som capaços de reconèixer objectes fins i tot quan s'observen sota unes condicions totalment diferents. Per exemple, som capaços de distingir un mateix objecte si l'observem des de diferents punts de vista, distància, condicions d'il·luminació, etc.La Visió per Computador intenta emular el sistema de visió humà mitjançant un sistema de captura d'imatges, un ordinador, i un conjunt de programes. L'objectiu desitjat no és altre que desenvolupar un sistema que pugui entendre una imatge d'una manera similar com ho realitzaria una persona.
Aquesta tesi es centra en l'anàlisi de la textura per tal de realitzar el reconeixement de superfícies. La motivació principal és resoldre el problema de la classificació de superfícies texturades quan han estat capturades sota diferents condicions, com ara distància de la càmera o direcció de la il·luminació. D'aquesta forma s'aconsegueix reduir els errors de classificació provocats per aquests canvis en les condicions de captura.
En aquest treball es presenta detalladament un sistema de reconeixement de textures que ens permet classificar imatges de diferents superfícies capturades en diferents condicions. El sistema proposat es basa en un model 3D de la superfície (que inclou informació de color i forma) obtingut mitjançant la tècnica coneguda com a 4-Source Colour Photometric Stereo (CPS). Aquesta informació és utilitzada posteriorment per un mètode de predicció de textures amb l'objectiu de generar noves imatges 2D de les textures sota unes noves condicions. Aquestes imatges virtuals que es generen seran la base del nostre sistema de reconeixement, ja que seran utilitzades com a models de referència per al nostre classificador de textures.
El sistema de reconeixement proposat combina les Matrius de Co-ocurrència per a l'extracció de característiques de textura, amb la utilització del Classificador del veí més proper. Aquest classificador ens permet al mateix temps aproximar la direcció d'il·luminació present en les imatges que s'utilitzen per testejar el sistema de reconeixement. És a dir, serem capaços de predir l'angle d'il·luminació sota el qual han estat capturades les imatges de test.
Els resultats obtinguts en els diferents experiments que s'han realitzat demostren la viabilitat del sistema de predicció de textures, així com del sistema de reconeixement.
This thesis is concerned with the application of texture analysis to discriminate between textured surfaces. The main motivation is the problem of classifying textured surfaces imaged under varying geometries, i.e. distance from the sensor and illumination direction, as well as the necessity of finding reliable methods of reducing classification errors caused by changes in the geometry's properties.
In texture analysis one must distinguish between image texture and surface texture. Image texture is what appears in the 2D image of a physical object, while surface texture refers to the variation of the physical and geometric properties of the imaged surface which give rise to the image texture. Changes in the imaging geometry can significantly alter the appearance of the surface, implying significant variations in the image texture. And one still has to perform the task of recognition from the image texture.
In this thesis, after analysing different strategies, we integrate the surface texture information derived by colour photometric stereo (CPS) into a complete model-based texture classification system. Photometric stereo is the technique which allows us to obtain surface texture information from a few images of the same surface imaged under various illumination directions. Basically, the main idea of our strategy consists of creating, by means of the surface texture information, a virtual' database of image textures against which we compare unknown test images in order to classify them. Note that we do not use the surface texture information directly to perform classification, but we use it to create new images which are the references for our training and classification process. Furthermore, the classification system allows us to guess the approximate direction of the illumination used to capture the test images.
The proposed prediction methods, as well as the model-based texture classification system, are tested and evaluated. A set of real surface textures containing a wide variety of relatively smooth and very rough surfaces are used in this thesis as our image database.
22
Dunlop, Jonathan. "Texture analysis in sonar images". Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340489.
23
Bravo, Maria Jacqueline Atoche [UNESP]. "Análise do descritor de padrões mapeados localmente em multiescala para classificação de textura em imagens digitais". Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/138320.
Abstract (sommario):
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
No presente trabalho, apresenta-se uma revisão sobre os principais abordagens para análise e classificação de texturas, entre eles o descritor LBP (Local Binary Pattern), o descritor LFP (Local Fuzzy Patterm) e o descritor MSLMP (Multi-scale Local Mapped Pattern), o qual é uma extensão multiescalar do descritor LMP (Local Mapped Pattern). Resultados anteriores presentes na literatura, indicaram que o MSLMP conseguiu resultados superiores aos mencionados anteriormente. Neste trabalho propõe-se uma análise mais abrangente sobre sua viabilidade para concluir que o MSLMP é mais eficaz que os anteriores. Essa análise é feita alterando-se a Matriz de Pesos para os pixels limiarizados. Para avaliar seu desempenho, foi utilizada a base de texturas do Album de Brodatz. Após processá-la pelo descritor MSLMP, com cada uma das matrizes de Pesos propostas neste trabalho, foram comparadas as taxas de acertos alcançadas usando a distância Chi-quadrado. Resultados experimentais mostram um valor de sensibilidade melhor para o descritor MSLMP em comparação aos outros descritores presentes na literatura.
This work, presents a review about the main techniques for analysis and classification of textures, including the LBP descriptor (Local Binary Pattern), the descriptor LFP (Local Fuzzy Pattern) and the descriptor MSLMP (Multi-Scale Local Mapped Pattern), which is a multi-scale extension of the LMP method (Local Mapped Pattern). Previous results present in the literature, indicated that the MSLMP achieved better results than those mentioned above. This work proposes a more comprehensive analysis of its feasibility to conclude that this descriptor is more effective than the others. This analysis is done by changing the weight matrix for the thresholding pixels. To evaluate its performance, it was used the texture base of the Brodatz album. After processing it by the descriptor MSLMP with each of the weights matrices proposed in this work, the achieved hit rates were compared by using the distance Chi-square. Experimental results show a better sensitivity value for MSLMP descriptor in comparison of other descriptors present in the literature.
CNPq: 131632/2014-0
24
Muñoz, Pujol Xavier 1976. "Image segmentation integrating colour, texture and boundary information". Doctoral thesis, Universitat de Girona, 2003. http://hdl.handle.net/10803/7719.
Abstract (sommario):
La tesis se centra en la Visión por Computador y, más concretamente, en la segmentación de imágenes, la cual es una de las etapas básicas en el análisis de imágenes y consiste en la división de la imagen en un conjunto de regiones visualmente distintas y uniformes considerando su intensidad, color o textura.Se propone una estrategia basada en el uso complementario de la información de región y de frontera durante el proceso de segmentación, integración que permite paliar algunos de los problemas básicos de la segmentación tradicional. La información de frontera permite inicialmente identificar el número de regiones presentes en la imagen y colocar en el interior de cada una de ellas una semilla, con el objetivo de modelar estadísticamente las características de las regiones y definir de esta forma la información de región. Esta información, conjuntamente con la información de frontera, es utilizada en la definición de una función de energía que expresa las propiedades requeridas a la segmentación deseada: uniformidad en el interior de las regiones y contraste con las regiones vecinas en los límites. Un conjunto de regiones activas inician entonces su crecimiento, compitiendo por los píxeles de la imagen, con el objetivo de optimizar la función de energía o, en otras palabras, encontrar la segmentación que mejor se adecua a los requerimientos exprsados en dicha función. Finalmente, todo esta proceso ha sido considerado en una estructura piramidal, lo que nos permite refinar progresivamente el resultado de la segmentación y mejorar su coste computacional.
La estrategia ha sido extendida al problema de segmentación de texturas, lo que implica algunas consideraciones básicas como el modelaje de las regiones a partir de un conjunto de características de textura y la extracción de la información de frontera cuando la textura es presente en la imagen.
Finalmente, se ha llevado a cabo la extensión a la segmentación de imágenes teniendo en cuenta las propiedades de color y textura. En este sentido, el uso conjunto de técnicas no-paramétricas de estimación de la función de densidad para la descripción del color, y de características textuales basadas en la matriz de co-ocurrencia, ha sido propuesto para modelar adecuadamente y de forma completa las regiones de la imagen.
La propuesta ha sido evaluada de forma objetiva y comparada con distintas técnicas de integración utilizando imágenes sintéticas. Además, se han incluido experimentos con imágenes reales con resultados muy positivos.
Image segmentation is an important research area in computer vision and many segmentation methods have been proposed. However, elemental segmentation techniques based on boundary or region approaches often fail to produce accurate segmentation results. Hence, in the last few years, there has been a tendency towards the integration of both techniques in order to improve the results by taking into account the complementary nature of such information. This thesis proposes a solution to the image segmentation integrating region and boundary information. Moreover, the method is extended to texture and colour texture segmentation.
An exhaustive analysis of image segmentation techniques which integrate region and boundary information is carried out. Main strategies to perform the integration are identified and a classification of these approaches is proposed. Thus, the most relevant proposals are assorted and grouped in their corresponding approach. Moreover, characteristics of these strategies as well as the general lack of attention that is given to the texture is noted. The discussion of these aspects has been the origin of all the work evolved in this thesis, giving rise to two basic conclusions: first, the possibility of fusing several approaches to the integration of both information sources, and second, the necessity of a specific treatment for textured images.
Next, an unsupervised segmentation strategy which integrates region and boundary information and incorporates three different approaches identified in the previous review is proposed. Specifically, the proposed image segmentation method combines the guidance of seed placement, the control of decision criterion and the boundary refinement approaches. The method is composed by two basic stages: initialisation and segmentation. Thus, in the first stage, the main contours of the image are used to identify the different regions present in the image and to adequately place a seed for each one in order to statistically model the region. Then, the segmentation stage is performed based on the active region model which allows us to take region and boundary information into account in order to segment the whole image. Specifically, regions start to shrink and expand guided by the optimisation of an energy function that ensures homogeneity properties inside regions and the presence of real edges at boundaries. Furthermore, with the aim of imitating the Human Vision System when a person is slowly approaching to a distant object, a pyramidal structure is considered. Hence, the method has been designed on a pyramidal representation which allows us to refine the region boundaries from a coarse to a fine resolution, and ensuring noise robustness as well as computation efficiency.
The proposed segmentation strategy is then adapted to solve the problem of texture and colour texture segmentation. First, the proposed strategy is extended to texture segmentation which involves some considerations as the region modelling and the extraction of texture boundary information. Next, a method to integrate colour and textural properties is proposed, which is based on the use of texture descriptors and the estimation of colour behaviour by using non-parametric techniques of density estimation. Hence, the proposed strategy of segmentation is considered for the segmentation taking both colour and textural properties into account.
Finally, the proposal of image segmentation strategy is objectively evaluated and then compared with some other relevant algorithms corresponding to the different strategies of region and boundary integration. Moreover, an evaluation of the segmentation results obtained on colour texture segmentation is performed. Furthermore, results on a wide set of real images are shown and discussed.
25
Linnett, L. M. "Multi-texture image segmentation". Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.
Abstract (sommario):
Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification.
26
Muhammad, Imran. "Colorizing Grey Scale Images". Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6181.
Abstract (sommario):
The purpose of this thesis is to develop a working methodology to color a grey scale image. This thesis is based on approach of using a colored reference image. Coloring grey scale images has no exact solution till date and all available methods are based on approximation. This technique of using a color reference image for approximating color information in grey scale image is among most modern techniques.Method developed here in this paper is better than existing methods of approximation of color information addition in grey scale images in brightness, sharpness, color shade gradients and distribution of colors over objects.Color and grey scale images are analyzed for statistical and textural features. This analysis is done only on basis of luminance value in images. These features are then segmented and segments of color and grey scale images are mapped on basis of distances of segments from origin. Then chromatic values are transferred between these matched segments from color image to grey scale image.Technique proposed in this paper uses better mechanism of mapping clusters and mapping colors between segments, resulting in notable improvement in existing techniques in this category.
27
Ingratta, Donato. "Texture image retrieval using fuzzy image subdivision". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0012/MQ52743.pdf.
28
Hao, Chuan Yan. "Image completion based on texture regularity and texture synthesis". Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1940411.
29
Rajagopal, S. (Satish). "3D texture reconstruction from multi-view images". Master's thesis, University of Oulu, 2017. http://urn.fi/URN:NBN:fi:oulu-201706022492.
Abstract (sommario):
Given an uncontrolled image dataset, there are several approaches to reconstruct the geometry of the scene and very few for reconstructing the texture. We analyze two different state of the art fully integrated texture reconstruction frameworks to generate a textured scene. Shan et al.’s approach [1] uses a shading model inspired by Computer Graphics rendering to formulate the scene and compute the texture. Texture is stored as albedo reflectance parameter per vertex for each color channel. Waechter et al. [2] uses a two-stage approach, first stage where a view is selected for each face and second stage where global and local adjustments are performed to smooth out seam visibility between patches. Both approaches have their own occlusion removal stage. We analyze these two drastically different approaches under different conditions. We compare the input images and rendered scenes from the same angle. We discuss about occlusion removal in an unconstrained image dataset. We modify the shading model proposed by Shan et al. to solve for a controlled indoor scene. The analysis shows the advantages of either approaches on specific conditions. The patch based texture reconstruction provides a visually appealing scene reconstructed in a considerable time. The vertex based texture reconstruction has a complex model providing us the framework to solve for lighting and environment conditions under which the images are captured. We believe that these two approaches provide fully integrated frameworks that reconstruct the scene for both geometry and texture from an uncontrolled image data set despite all the inherent challenges in a reasonable time.
30
Montoya, Zegarra Javier Alexandre. "Descrição de texturas invariante a rotação e escala para identificação e reconhecimento de imagens". [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276078.
Abstract (sommario):
Orientadores: Neucimar Jeronimo Leite, Ricardo da Silva TorresDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: Uma importante característica de baixo nível, utilizada tanto na percepção humana como no reconhecimento de padrões, é a textura. De fato, o estudo de textura tem encontrado diversas aplicações abrangendo desde segmentação de textura até síntese, classificação e recuperação de imagens por conteúdo.Apesar das múltiplas técnicas eficientes e eficazes propostas para classificação e recuperação, ainda há alguns desafios que precisam ser superados como, por exemplo, a necessidade de descritores de imagens compactos e robustos a serem empregados na consulta e classificação de bases de imagens de textura. Esta dissertação propõe um descritor de imagens de textura visando à busca e à recuperação de bases de dados de imagens. Este descritor baseia-se na Decomposição Piramidal Steerable caracterizada por sua análise de forma invariante à rotação ou à escala. Resultados preliminares conduzidos em cenários não-controlados demonstraram caráter promissor da abordagem. No que diz respeito à classificação de imagens de textura, esta dissertação propõe ao mesmo tempo um sistema de reconhecimento, o qual possui como principais características representações compactas de imagens e módulos de reconhecimento eficientes. O descritor proposto é utilizado para codificar a informação relevante de textura em vetores de características pequenos. Para tratar os requisitos de eficiência do reconhecimento, uma abordagem multi-classe baseada no classificador de Floresta de Caminhos Ótimos é utilizada. Experimentos foram condl!zidos visando avaliar o sistema proposto frente a outros métodos de classificação. Resultados experimentais demonstram a superioridade do sistema proposto
Abstract: An important low-level image feature used in human perception as well as in recognition is texture. 1n fact, the study of texture has found several applications ranging from texture segmentation to texture synthesis, classification, and image retrieval. Although many efficient and effective techniques have been proposed for texture classification and retrieval, there are still some challenges to overcome. More specifically, there is a need for a compact and robust image descriptor to query and classify texture image databases. 1n order to search and query image databases, this dissertation provides a texture image descriptor, which is based on a modification of the Steerable Pyramid Decomposition, and is also characterized by its capabilities for representing texture images in either rotation-invariant or scale-invariant manners. Preliminary results conducted in non-controlled scenarios have demonstrated the promising properties of the approach. 1n order to classify texture im.ages, this dissertation also provides a new recognition system, which presents as main features, compact image representations and efficient recognition tasks. The proposed image descriptor is used to encode the relevant texture information in small size feature vectors. To address the efficiency recognition requirements, a novel multi-class object recognition method based on the Optimum Path Forest classifier is used. To evaluate our proposed system against different methods, several experiments were conducted. The results demonstrate the superiority of the proposed system
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
31
Alegro, Maryana de Carvalho. "Segmentação de tumores de encéfalo em imagens por ressonância magnética baseada em informações texturais". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-11082009-170102/.
Abstract (sommario):
As imagens por ressonância magnéticas não indispensáveis no diagnóstico e tratamento de tumores do encéfalo devido ao seu alto grau de detalhamento anatômico. A tarefa de segmenta¸cão da região tumoral, nestas, permite uma análise quantitativa mais precisa, viabilizando um melhor acompanhamento da evolução/regressão da doença. Porém, a realização manual de tal trabalho é cansativa e apresenta diversas desvantagens que a tornam proibitiva, fazendo com que nao haja muitos médicos dispostos a realizá-la rotineiramente. Neste trabalho é proposto um sistema para segmenta¸cão automática de tumores do encéfalo. O sistema emprega parâmetros de textura de naturezas diversas, como estatísticos, baseados em modelo, e baseados em transformada, os quais são extraídos de diferentes tipos de imagem comuns à pratica médica (T1, T1 com contraste e FLAIR). As técnicas de análise de textura são capazes de detectar alterações mínimas nos tecidos, às vezes imperceptíveis à visão humana, fato que motiva sua adoção; e podem ser complementadas por informações adicionais como valores de intensidade. O sistema proposto conta com quatro etapas básicas: pré-processamento, extração de características, segmentação e pós-processamento; e baseia-se no uso de uma máquina de vetor de suporte para classificação dos pixeis. Os resultados obtidos mostram que o sistema apresenta uma taxa média de acerto elevada, comparável aos resultados encontrados em trabalhos relacionados, sendo capaz de localizar e delimitar a região tumoral sem necessidade de interação com o usuário. A quantificação dos resultados foi realizada utilizando-se métricas de artigos encontrados na literatura.Magnetic resonance images are essential in the diagnosing and treatment of brain tumors due to its high amount of anatomic details. The task of segmenting brain tumor regions in these images makes more exact quantitative analysis feasible, allowing a better tracking of the evolution/regression of the disease. Nevertheless, the execution of such task is burdensome, featuring several drawbacks that turns it into a prohibitive one, and makes many doctors unwilling to put it into practice. In this work an automatic brain tumor segmentation system is proposed, in which several types of texture parameters such as statistical, model based and transform based, are applied. Those parameters are extracted from different, extensively used, types of magnetic resonance images (T1, T1 with contrast and FLAIR). Texture analysis techniques are capable of detecting tiny changes in underlying tissue, which are sometimes imperceptible to the human vision, fact that motivates its adoption here. Texture features can also be completed by other kinds of characteristics, such as pixel intensity. The proposed system comprises four basic steps: pre-processing, feature extraction, segmentation, and post-processing, and is based on a support vector machine for pixel classification. Final results shows that the system archived high success rates, which are comparable to results found in related works, and that it was able to locate and delimit tumor areas without any user interaction. For the quantification of the results, some metrics found in papers presented in the literature were adopted.
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AbdelNasser, Mohamed Mahmoud Mohamed. "Development of advanced computer methods for breast cancer image interpretation through texture and temporal evolution analysis". Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/395213.
Abstract (sommario):
El càncer de pit és una de les malalties més perilloses que ataquen les dones. Els sistemes de diagnòstic assistit per ordinador poden ajudar a detectar el càncer de pit de forma precoç i reduir-ne la mortalitat. Aquesta tesi proposa diversos mètodes per a l'anàlisi d'imatges de càncer de mama. Analitzem el càncer de mama a mamografies, ecografies i termografies. La nostra anàlisi inclou la classificació de massa / teixit normal de pit, la classificació de tumors benignes / maligne en les mamografies i les imatges d'ultrasò, detecció de mugró en termogrames, registre de mamografies i l'anàlisi de l'evolució dels tumors de pit.
Es van considerar mètodes coneguts d'anàlisis de textures i s'han proposat dos nous descriptors de textura. També es va estudiar l'efecte de la resolució de píxels, l'escala d'integració, el pre-processament i la normalització en el rendiment d'aquests mètodes d'anàlisi de textures per a la classificació dels tumors. Finalment, hem utilitzat la tècnica de super-resolució per millorar el funcionament dels mètodes d'anàlisi de textures a l'hora de classificar els tumors de pit en les imatges d'ultrasò.
Per a l'anàlisi del càncer de pit a termogrames, proposem un mètode automàtic per a la detecció dels mugrons que és precís i senzill. Per analitzar l'evolució del càncer de pit, es proposa un mètode de registre temporal de mamografies basat en coordenades curvilínies. També proposem un mètode per quantificar i visualitzar l'evolució dels tumors de pit en pacients sotmesos a tractament mèdic. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels mètodes de l'estat de l'art i poden ajudar a millorar el diagnòstic del càncer de pit.El cáncer de mama es una de las enfermedades más peligrosas que afecta a las mujeres. Los sistemas de diagnóstico asistido por ordenador pueden ayudar a detectar el cáncer de mama de una manera temprana y reducir la mortalidad. Esta tesis propone varios métodos para el análisis de imágenes de cáncer de mama. Analizamos el cáncer de mama en mamografías, ecografías y termografías. Nuestro análisis incluye la clasificación de masa / tejido normal de mama, la clasificación de tumores benignos / malignos en mamografías e imágenes de ultrasonido, la detección del pezón en termogramas, el registro de mamografías y el análisis de la evolución de los tumores de mama. Consideramos métodos bien conocidos de análisis de texturas y propusimos dos nuevos descriptores de texturas. También estudiamos el efecto de la resolución de los píxeles, la escala de integración, el pre-procesamiento y la normalización de las características en el rendimiento de estos métodos de análisis de texturas para la clasificación de los tumores. Finalmente, hemos utilizado la técnica de super-resolución para mejorar el rendimiento de estos métodos de análisis de texturas a la hora de clasificar los tumores de mama en imágenes de ultrasonido. Para el análisis del cáncer de mama en termogramas, proponemos un método automático para la detección precisa y sencilla de los pezones. Para analizar la evolución del cáncer de mama, proponemos un método de registro de mamografía temporal basado en coordenadas curvilíneas. También proponemos un método para cuantificar y visualizar la evolución de los tumores de mama en pacientes sometidos a tratamiento médico. En general, los métodos propuestos en esta tesis mejoran el rendimiento de las aproximaciones que se encuentran en el estado del arte y pueden ayudar a mejorar el diagnóstico del cáncer de mama.
Breast cancer is one of the most dangerous diseases that attacks women. Computer-aided diagnosis systems may help to detect breast cancer early and reduce mortality. This thesis proposes several methods for analyzing breast cancer images. We analyze breast cancer in mammographies, ultrasonographies and thermographies. Our analysis includes mass/normal breast tissue classification, benign/malignant tumor classification in mammograms and ultrasound images, nipple detection in thermograms, mammogram registration and analysis of the evolution of breast tumors. We considered well-known texture analysis methods and proposed two new texture descriptors. We also studied the effect of pixel resolution, integration scale, preprocessing and feature normalization on the performance of these texture analysis methods for tumor classification. Finally, we used super-resolution approaches to improve the performance of texture analysis methods when classifying breast tumors in ultrasound images. For the analysis of breast cancer in thermograms, we propose an automatic method for detecting nipples that is accurate and simple. To analyze the evolution of breast cancer, we propose a temporal mammogram registration method based on curvilinear coordinates. We also propose a method for quantifying and visualizing the evolution of breast tumors in patients undergoing medical treatment. Overall, the methods proposed in this thesis improve the performance of the state-of-the-art approaches and may help to improve the diagnosis of breast cancer.
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Azzabou, Noura. "Restauration des images naturelles et préservation de la texture à l'aide de noyaux de taille normale". Phd thesis, Ecole des Ponts ParisTech, 2008. http://pastel.archives-ouvertes.fr/pastel-00004041.
Abstract (sommario):
Cette thèse s'intéresse aux problèmes de restauration d'images et de préservation de textures. Cette tache nécessite un modèle image qui permet de caractériser le signal qu'on doit obtenir. Un tel model s'appuie sur la définition de l'interaction entre les pixels et qui est caractérisé par deux aspects : (i) la similarité photométrique entre les pixels (ii) la distance spatiale entre les pixels qui peut être comparée à une grandeur d'échelle. La première partie de la thèse introduit un nouveau modèle non paramétrique d'image. Ce modèle permet d'obtenir une description adaptative de l'image en utilisant des noyaux de taille variable obtenue `a partir d'une étape de classification effectuée au préalable. La deuxième partie introduit une autre approche pour décrire la dépendance entre pixels d'un point de vue géométrique. Ceci est effectué `a l'aide d'un modèle statistique de la co-occurrence entre les observations de point de vue géométrique. La dernière partie est une nouvelle technique de sélection automatique (pour chaque pixel) de la taille des noyaux utilisé au cours du filtrage. Cette thèse est conclue avec l'application de cette dernière approche dans différents contextes de filtrage ce qui montre sa flexibilité vis-à-vis des contraintes liées aux divers problèmes traités.
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Petroudi, Styliani. "Texture in mammographic image analysis". Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422668.
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Byrne, James. "Texture synthesis for image compression". Thesis, University of Bristol, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574259.
Abstract (sommario):
Still image compression methods have changed little over the last ten years. Mean- while, the quantity of content transmitted over limited bandwidth channels has increased dramatically. The currently available methods are content agnostic: that I is they use the same compression process independent of the content at any given spatial location. Region specific coding provides one possible route to increased compression performance. Texture regions in particular are usually not conceptually important to a viewer of an image, but the high frequency nature of such regions consumes many bits when encoding. Texture synthesis is the process of generating textures from a sample or parameter set, and thus if these texture regions can be encoded by spec- ifying texture synthesis at the decoder, it may be possible to save large amounts of data, without detriment to the decoded image quality. This thesis presents a number of adaptations to the Graphcut patch based texture synthesis method, to make it suitable for constrained synthesis of texture regions in natural images. This includes a colour matching process to account for luminance and chrominance changes over the texture region, and a modification to allow constrained synthesis of an arbitrarily shaped region. This architecture is then integrated into two complete image compression by synthesis systems based on JPEG and JPEG2000 respectively. In each case the image is segmented, anal- ysed and synthesis occurs at the decoder to fill in removed texture regions. In the system based on JPEG2000 a feedback loop is included which makes some assess- ment of the quality of the synthesis at the encoder in order to adapt the synthesis parameters to improve the result quality, or to skip synthesis entirely if deemed necessary. The results of these systems show some promise in that substantial savings can be made over transform coded images coded at the same Q value as the residual image. However it is observed that synthesis can be detrimental to the quality of the image in comparison to an equivalent traditionally coded image at the same bitrate. Two methods of texture orientation analysis for non-homogeneous textures are presented. One of these in particular produces a good assessment of the texture orientation. This method uses a Steerable Pyramid transform to analyse the orientations. Then, two methods of sample selection and synthesis using the analysed texture orientation are presented. These methods aim to recreate the original texture's orientation variation from a smaller texture sample and the orientation map. The best of these methods selects one or more samples containing multiple orientations and selects texture patches appropriately oriented to the current location of synthesis.
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Girometti, Laura. "Automatic texture-cartoon image decomposition". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24486/.
Abstract (sommario):
La decomposizione di un'immagine nelle sue componenti più significative, come la struttura, la texture e il rumore, svolge un ruolo chiave nel poter processare al meglio l'immagine stessa. L'obiettivo di questa tesi è di proporre un modello a due fasi per decomporre un'immagine in tre componenti, ovvero cartoon-texture-rumore, utilizzando un approccio variazionale, che consiste nel minimizzare un funzionale costituito da più termini energia, ognuno adatto ad estrarre una specifica componente, bilanciati da diversi parametri. Lo scopo è riuscire a meglio separare la texture dal rumore, data la natura oscillante di entrambe le componenti che le rende difficilmente distinguibili. Inoltre, viene proposto e analizzato numericamente un principio di cross-correlation per settare automaticamente il parametro che bilancia i termini nel funzionale energia, data la sua influenza sulla qualità della decomposizione finale.
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Castelano, Célio Ricardo. "Estudo comparativo da transformada wavelet no reconhecimento de padrões da íris humana". Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-30112006-134736/.
Abstract (sommario):
Neste trabalho é apresentado um método para reconhecimento de seres humanos através da textura da íris. A imagem do olho é processada através da análise do gradiente, com uma técnica de dispersão aleatória de sementes. Um vetor de características é extraído para cada íris, baseado na análise dos componentes wavelet em diversos níveis de decomposição. Para se mensurar as distâncias entre esses vetores foi utilizado o cálculo da distância Euclidiana, gerando-se curvas recall x precision para se medir a eficiência do método desenvolvido. Os resultados obtidos com algumas famílias wavelets demonstraram que o método proposto é capaz de realizar o reconhecimento humano através da íris com uma precisão eficiente.This work presents a method for recognition of human beings by iris texture. The image of the eye is processed through gradient analysis, based on a random dispersion of seeds. So, it is extracted a feature vector for each iris based on wavelet transform in some levels of decomposition. To estimate the distances between these vectors it was used the Euclidean distance, and recall x precision curves are generated to measure the efficiency of the developed method. The results gotten with some wavelets families had demonstrated that the proposed methodology is capable to do human recognition through the iris with an efficient precision.
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Nyman, Anton. "DETECTION OF ANOMALIES IN IMAGES OF HOMOGENEOUS TEXTURES". Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-186998.
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Vieira, Fábio Henrique Antunes [UNESP]. "Image processing through machine learning for wood quality classification". Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/142813.
Abstract (sommario):
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A classificação da qualidade da madeira é indicada para indústria de processamento e produção desse material. Essas empresas têm investido em soluções para agregar valor à matéria-prima, com o intuito de melhorar resultados, observando os rumos do mercado. O objetivo deste trabalho foi comparar Redes Neurais Convolutivas, um método de aprendizado profundo, na classificação da qualidade de madeira, com outras técnicas tradicionais de Máquinas de aprendizado, como Máquina de Vetores de Suporte, Árvores de Decisão, Regra dos Vizinhos Mais Próximos e Redes Neurais, em conjunto com Descritores de Textura. Isso foi possível através da verificação do nível de acurácia das experiências com diferentes técnicas, como Aprendizado Profundo e Descritores de Textura no processamento de imagens destes objetos. Foi utilizada uma câmera convencional para capturar as 374 amostras de imagem adotadas no experimento, e a base de dados está disponível para consulta. O processamento das imagens passou por algumas fases, após terem sido obtidas, como pré-processamento, segmentação, análise de recursos e classificação. Os métodos de classificação se deram através de Aprendizado Profundo e por meio de técnicas de Aprendizado de Máquinas tradicionais como Máquina de Vetores de Suporte, Árvores de Decisão, Regra dos Vizinhos Mais Próximos e Redes Neurais juntamente com os Descritores de Textura. Os resultados empíricos para o conjunto de dados das imagens da madeira serrada mostraram que o método com Descritores de Textura, independentemente da estratégia empregada, foi muito competitivo quando comparado com as Redes Neurais Convolutivas para todos os experimentos realizados, e até mesmo superou-as para esta aplicação.
The quality classification of wood is prescribed throughout the wood chain industry, particularly those from the processing and manufacturing fields. Those organizations have invested energy and time trying to increase value of basic items, with the purpose of accomplishing better results, in agreement to the market. The objective of this work was to compare Convolutional Neural Network, a deep learning method, for wood quality classification to other traditional Machine Learning techniques, namely Support Vector Machine (SVM), Decision Trees (DT), K-Nearest Neighbors (KNN), and Neural Networks (NN) associated with Texture Descriptors. Some of the possible options were to assess the predictive performance through the experiments with different techniques, Deep Learning and Texture Descriptors, for processing images of this material type. A camera was used to capture the 374 image samples adopted on the experiment, and their database is available for consultation. The images had some stages of processing after they have been acquired, as pre-processing, segmentation, feature analysis, and classification. The classification methods occurred through Deep Learning, more specifically Convolutional Neural Networks - CNN, and using Texture Descriptors with Support Vector Machine, Decision Trees, K-nearest Neighbors and Neural Network. Empirical results for the image dataset showed that the approach using texture descriptor method, regardless of the strategy employed, is very competitive when compared with CNN for all performed experiments, and even overcome it for this application.
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Ha, Minh Thien. "Modelling of stochastic and quasi-periodic texture images /". [S.l.] : [s.n.], 1989. http://library.epfl.ch/theses/?nr=804.
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Song, Keng Yew. "Surface defect detection on textured background". Thesis, University of Surrey, 1993. http://epubs.surrey.ac.uk/844113/.
Abstract (sommario):
This thesis addresses the problem of defect detection on complex textural surfaces. In general, whether the texture to be inspected is regular or random, in image terms it is characterized by local variations in pixel grey level values. These normal variations render the problem of texture defect detection extremely difficult as defects are often manifested by grey level changes and their detection requires more than mere pixel comparisons. In the thesis, classical techniques on texture representation are studied and various existing texture defect detection algorithms are reviewed. Three novel algorithms have been developed to tackle the problem of defect detection on random or regular textures. The first two are devoted to the problem of crack detection and the third algorithm is devoted to the problem of detecting regional defects. For texture crack detection, a cojoint spatial and spatial frequency representation, that is, wigner distribution is proposed to model the inspected texture surface. A detailed analysis of the wigner distribution, its properties and the effect of windowing on its crack detection performance are carried out. Two postprocessing methods, ie, probabilistic relaxation labelling and linear filtering are incorporated into the crack detection algorithm to refine the results. The potential of the Wigner model has also been explored by modifying the crack detection algorithm so as to detect other types of defects. For real world applications, an efficient crack detection algorithm based on a new distribution is proposed. The algorithm is shown to produce comparable results but in much shorter time. For regional defect detection, a hybrid chromato-structural approach to colour texture representation is proposed where combined colour texture information is extracted from various chromatic classes associated with the inspected surface. In the approach, a unified defect detection framework which combines a new colour clustering scheme, morphological smoothing and blob analysis are used to capture the relevant combined colour texture information. With this framework, good defect detection results are obtained and presented in this thesis.
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Galerne, Bruno. "Stochastic image models and texture synthesis". Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00595283.
Abstract (sommario):
Cette thèse est une étude de modèles d'image aléatoires avec des applications en synthèse de texture.Dans la première partie de la thèse, des algorithmes de synthèse de texture basés sur le modèle shot noise sont développés. Dans le cadre discret, deux processus aléatoires, à savoir le shot noise discret asymptotique et le bruit à phase aléatoire, sont étudiés. On élabore ensuite un algorithme rapide de synthèse de texture basé sur ces processus. De nombreuses expériences démontrent que cet algorithme permet de reproduire une certaine classe de textures naturelles que l'on nomme micro-textures. Dans le cadre continu, la convergence gaussienne des modèles shot noise est étudiée d'avantage et de nouvelles bornes pour la vitesse de cette convergence sont établies. Enfin, on présente un nouvel algorithme de synthèse de texture procédurale par l'exemple basé sur le récent modèle Gabor noise. Cet algorithme permet de calculer automatiquement un modèle procédural représentant des micro-textures naturelles.La deuxième partie de la thèse est consacrée à l'étude du processus feuilles mortes transparentes (FMT), un nouveau modèle germes-grains obtenu en superposant des objets semi-transparents. Le résultat principal de cette partie montre que, lorsque la transparence des objets varie, le processus FMT fournit une famille de modèles variant du modèle feuilles mortes à un champ gaussien. Dans la troisième partie de la thèse, les champs aléatoires à variation bornés sont étudiés et on établit des résultats généraux sur le calcul de la variation totale moyenne de ces champs. En particulier, ces résultats généraux permettent de calculer le périmètre moyen des ensembles aléatoires et de calculer explicitement la variation totale moyenne des modèles germes-grains classiques.
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Tan, Tieniu. "Image texture analysis : classification and segmentation". Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.
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Chen, Yan Qiu. "Novel techniques for image texture classification". Thesis, University of Southampton, 1995. https://eprints.soton.ac.uk/250162/.
Abstract (sommario):
Texture plays an increasingly important role in computer vision. It has found wide application in remote sensing, medical diagnosis, quality control, food inspection and so forth. This thesis investigates the problem of classifying texture in digital images, following the convention of splitting the problem into feature extraction and classification. Texture feature descriptions considered in this thesis include Liu's features, features from the Fourier transform using geometrical regions, the Statistical Gray-Level Dependency Matrix, and the Statistical Feature Matrix. Classification techniques that are considered in this thesis include the K-Nearest Neighbour Rule and the Error Back-Propagation method. Novel techniques developed during the author's Ph.D study include (1) a Generating Shrinking Algorithm that builds a three-layer feed-forward network to classify arbitrary patterns with guaranteed convergence and known generalisation behaviour, (2) a set of Statistical Geometrical Features for texture analysis based on the statistics of the geometrical properties of connected regions in a sequence of binary images obtained from a texture image, (3) a neural implementation of the K-Nearest Neighbour Rule that can complete a classification task within 2K clock cycles. Experimental evaluation using the entire Brodatz texture database shows that (1) the Statistical Geometrical Features give the best performance for all the considered classifiers, (2) the Generating Shrinking Algorithm offers better performance over the Error Back-Propagation method and the K-Nearest Neighbour Rule's performance is comparable to that of the Generating Shrinking Algorithm, (3) the combination of the Statistical Geometrical Features with the Generating-Shrinking Algorithm constitutes one of the best texture classification systems considered.
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Alaei, Fahimeh. "Texture Feature-based Document Image Retrieval". Thesis, Griffith University, 2019. http://hdl.handle.net/10072/385939.
Abstract (sommario):
Storing and manipulating documents in digital form to contribute to a paperless society has been the propensity of emerging technology. There has been notable growth in the variety and quantity of digitised documents, which have often been scanned/photographed and archived as images without any labelling or sufficient index information. The growth of these kinds of document images will undoubtedly continue with new technology. To provide an effective way for retrieving and organizing these document images, many techniques have been implemented in the literature. However, designing automation systems to accurately retrieve document images from archives remains a challenging problem. Finding discriminative and effective features is the fundamental task for developing an efficient retrieval system. An overview of the literature reveals that research on document image retrieval using texture-based features has not yet been broadly investigated. Texture features are suitable for large volume data and are generally fast to compute. In this study, the effectiveness of more than 50 different texture-based feature extraction methods from four categories of texture features - statistical, transform-based, model-based, and structural approaches - are investigated in order to propose a more accurate method for document image retrieval. Moreover, the influence of resolution and similarity metrics on document image retrieval are examined. The MTDB, ITESOFT, and CLEF_IP datasets, which are heterogeneous datasets providing a great variety of page layouts and contents, are considered for experimentation, and the results are computed in terms of retrieval precision, recall, and F-score. By considering the performance, time complexity, and memory usage of different texture features on three datasets, the best category of texture features for obtaining the best retrieval results is discussed. The effectiveness of the transform-based category over other categories in regard to obtaining higher retrieval result is proven. Many new feature extraction and document image retrieval methods are proposed in this research. To attain fast document image retrieval, the number of extracted features and time complexity play a significant role in the retrieval process. Thus, a fast and non-parametric texture feature extraction method based on summarising the local grey-level structure of the image is further proposed in this research work. The proposed fast local binary pattern provided promising results, with lower computing time as well as smaller memory space consumption compared to other variations of local binary pattern-based methods. There is a challenge in DIR systems when document images in queries are of different resolutions from the document images considered for training the system. In addition, a small number of document image samples with a particular resolution may only be available for training a DIR system. To investigate these two issues, an under-sampling concept is considered to generate under-sampled images and to improve the retrieval results. In order to use more than one characteristic of document images for document image retrieval, two different texture-based features are used for feature extraction. The fast-local binary method as a statistical approach, and a wavelet analysis technique as a transform-based approach, are used for feature extraction, and two feature vectors are obtained for every document image. The classifier fusion method using the weighted average fusion of distance measures obtained in relation to each feature vector is then proposed to improve document image retrieval results. To extract features similar to human visual system perception, an appearance-based feature extraction method for document images is also proposed. In the proposed method, the Gist operator is employed on the sub-images obtained from the wavelet transform. Thereby, a set of global features from the original image as well as sub-images are extracted. Wavelet-based features are also considered as the second feature set. The classifier fusion technique is finally employed to find similarity distances between the extracted features using the Gist and wavelet transform from a given query and the knowledge-base. Higher document image retrieval results have been obtained from this proposed system compared to the other systems in the literature. The other appearance-based document image retrieval system proposed in this research is based on the use of a saliency map obtained from human visual attention. The saliency map obtained from the input document image is used to form a weighted document image. Features are then extracted from the weighted document images using the Gist operator. The proposed retrieval system provided the best document image retrieval results compared to the results reported from other systems. Further research could be undertaken to combine the properties of other approaches to improve retrieval result. Since in the conducted experiments, a priori knowledge regarding document image layout and content has not been considered, the use of prior knowledge about the document classes may also be integrated into the feature set to further improve the retrieval performanceThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Tania, Sheikh. "Efficient texture descriptors for image segmentation". Thesis, Federation University Australia, 2022. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184087.
Abstract (sommario):
Colour and texture are the most common features used in image processing and computer vision applications. Unlike colour, a local texture descriptor needs to express the unique variation pattern in the intensity differences of pixels in the neighbourhood of the pixel-of-interest (POI) so that it can sufficiently discriminate different textures. Since the descriptor needs spatial manipulation of all pixels in the neighbourhood of the POI, approximation of texture impacts not only the computational cost but also the performance of the applications. In this thesis, we aim to develop novel texture descriptors, especially for hierarchical image segmentation techniques that have recently gained popularity for their wide range of applications in medical imaging, video surveillance, autonomous navigation, and computer vision in general. To pursue the aim, we focus in reducing the length of the texture feature and directly modelling the distribution of intensity-variation in the parametric space of a probability density function (pdf). In the first contributory chapter, we enhance the state-of-the-art Weber local descriptor (WLD) by considering the mean value of neighbouring pixel intensities along radial directions instead of sampling pixels at three scales. Consequently, the proposed descriptor, named Radial Mean WLD (RM-WLD), is three-fold shorter than WLD and it performs slightly better than WLD in hierarchical image segmentation. The statistical distributions of pixel intensities in different image regions are diverse by nature. In the second contributory chapter, we propose a novel texture feature, called ‘joint scale,’ by directly modelling the probability distribution of intensity differences. The Weibull distribution, one of the extreme value distributions, is selected for this purpose as it can represent a wide range of probability distributions with a couple of parameters. In addition, gradient orientation feature is calculated from all pixels in the neighbourhood with an extended Sobel operator, instead of using only the vertical and horizontal neighbours as considered in WLD. The length of the texture descriptor combining joint scale and gradiet orientation features remains the same as RM-WLD, but it exhibits significantly improved discrimination capability for better image segmentation. Initial regions in hierarchical segmentation play an important role in approximating texture features. Traditional arbitrary-shaped initial regions maintain the uniform colour property and thus may not retain the texture pattern of the segment they belong to. In the final contributory chapter, we introduce regular-shaped initial regions by enhancing the cuboidal partitioning technique, which has recently gained popularity in image/video coding research. Since the regions (cuboids) of cuboidal partitioning are of rectangular shape, they do not follow the colour-based boundary adherence of traditional initial regions. Consequently, the cuboids retain sufficient texture pattern cues to provide better texture approximation and discriminating capability. We have used benchmark segmentation datasets and metrics to evaluate the proposed texture descriptors. Experimental results on benchmark metrics and computational time are promising when the proposed texture features are used in the state-of-the-art iterative contraction and merging (ICM) image segmentation technique.Doctor of Philosophy
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Elunai, Ronald Tabu. "Particulate texture image analysis with applications". Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/45718/1/Ronald_Elunai_Thesis.pdf.
Abstract (sommario):
Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.
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Klimeš, Jiří. "Analýza textury objektů v zorném poli kamery". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442465.
Abstract (sommario):
This master thesis deals with design and implementation of algorithms for image analysis of object surface texture for the purpose of automating the surface grinding process. In the first part of this thesis, a search was performed in the field of image analysis of object surface texture. The proposed descriptors were tested on the created annotated database of texture images. Subsequently, a scene for image acquisition of the machined object was designed and assembled, and the grinding process was automated based on the results of the previous analysis. The implementation and achieved results were evaluated and other possible improvements were proposed.
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Silva, Núbia Rosa da. "Reconhecimento de padrões heterogêneos e suas aplicações em biologia e nanotecnologia". Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30032016-145929/.
Abstract (sommario):
O reconhecimento de padrões de textura em imagens tem sido uma importante ferramenta na área de visão computacional. Isso porque o atributo textura pode revelar características intrínsecas, tornando possível a classificação de um conjunto de imagens semelhantes. Embora a textura seja estudada há mais de meio século, ainda não existe um consenso sobre sua definição e nem mesmo um método de extração de características de textura que seja eficiente para todos os tipos de imagens. Além disso, os métodos da literatura analisam os padrões de textura de maneira global, considerando que uma imagem apresente um conjunto de micropadrões que formam um único padrão global ou homogêneo de textura na imagem. No entanto, alguns tipos de imagens apresentam heterogeneidade em sua composição, ou seja, o conjunto de micropadrões na imagem é responsável por formar mais de um padrão de textura dentro da mesma imagem. Esse tipo de imagens levou ao propósito de investigação deste trabalho. Independentemente do método de extração de característica utilizado, considerar a heterogeneidade do padrão de textura em uma imagem leva a uma melhor representação de suas características. Para melhorar a análise de padrões heterogêneos de textura, três abordagens são propostas: (i) lazy-patch, (ii) combinação de modelos e (iii) modelagem da textura por meio de autômatos celulares inspirados em corrosão alveolar. Os resultados ao aplicar essas abordagens em diferentes conjuntos de imagens de biologia e nanotecnologia, mostraram que a análise de padrões heterogêneos resulta em melhor representatividade de imagens que possuem padrões heterogêneos de textura em sua composição.Pattern recognition of texture in images has been playing an important role in computer vision area. This is because the texture attribute can reveal intrinsic characteristics, making it possible to classify a set of similar images. Although the texture is studied for over half a century, there is still no consensus on its definition or even a method to extrac texture characteristics that is effective for all types of images. Moreover, literature methods globally analyze the texture patterns, whereas a picture displays a number of micropatterns which form a single homogenous global pattern of texture in the image. However, some types of image display heterogeneity in their composition, that is, the set of micropatterns in the image use to form more than one texture pattern within the same image. Such type of image led to the purpose of this research work. Regardless the feature extraction method used, considering the heterogeneity of the texture pattern in an image leads to better representation of its features. To further improve the analysis of heterogeneous texture patterns, three approaches are proposed: (i) lazy-patch, (ii) combination of models and (iii) texture modeling using cellular automata inspired by pitting corrosion. The results of applying these approaches in different sets of biology and nanotechnology images showed that the analysis of heterogeneous patterns results in better representation of images that have heterogeneous patterns of texture in your composition.
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Camilleri, Kenneth P. "Multiresolution texture segmentation". Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.
Abstract (sommario):
The problem of unsupervised texture segmentation was studied and a texture segmentation algorithm was developed making use of the minimum number of prior assumptions. In particular, no prior information about the type of textures, the number of textures and the appropriate scale of analysis for each texture was required. The texture image was analysed by the multiresolution Gabor expansion. The Gabor expansion generates a large number of features for each image and the most suitable feature space for segmentation needs to be determined automatically. The two-point correlation function was used to test the separability of the distributions in each feature space. A measure was developed to evaluate evidence of multiple clusters from the two-point correlation function, making it possible to determine the most suitable feature space for clustering. Thus, at a given resolution level, the most appropriate feature space was selected and used to segment the image. Due to inherent ambiguities and limitations of the two-point correlation function, this feature space exploration and segmentation was performed several times at the same resolution level until no further evidence of multiple clusters was found, at which point, the process was repeated at the next finer resolution level. In this way, the image was progressively segmented, proceeding from coarse to fine Gabor resolution levels without any knowledge of the actual number of textures present. In order to refine the region-labelled image obtained at the end of the segmentation process, two postprocessing pixel-level algorithms were developed and implemented. The first was the mixed pixel classification algorithm which is based on the analysis of the effect of the averaging window at the boundary between two regions and re-assigns the pixel labels to improve the boundary localisation. Multiresolution probabilistic relaxation is the second postprocessing algorithm which we developed. This algorithm incorporates contextual evidence to relabel pixels close to the boundary in order to smooth it and improve its localisation. The results obtained were quantified by known error measures, as well as by new error measures which we developed. The quantified results were compared to similar results by other authors and show that our unsupervised algorithm performs as well as other methods which assume prior information.