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Li, Zhongqiang. "Segmentation of textured images". Thesis, University of Central Lancashire, 1991. http://clok.uclan.ac.uk/20270/.
Pełny tekst źródłaLeng, Xiaoling. "Analysis of some textured images by transputer". Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324405.
Pełny tekst źródłaNoriega, Leonardo Antonio. "The colorimetric segmentation of textured digital images". Thesis, Southampton Solent University, 1998. http://ssudl.solent.ac.uk/2444/.
Pełny tekst źródłaBradbury, 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.
Pełny tekst źródłaWilliams, 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.
Pełny tekst źródłaVăcar, Cornelia Paula. "Inversion for textured images : unsupervised myopic deconvolution, model selection, deconvolution-segmentation". Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0131/document.
Pełny tekst źródłaThis 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
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.
Pełny tekst źródłaEsta 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.
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.
Pełny tekst źródłaAchddou, Raphaël. "Synthetic learning for neural image restoration methods". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT006.
Pełny tekst źródłaPhotography 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
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.
Pełny tekst źródłaFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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.
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.
Pełny tekst źródłaSiqueira, 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.
Pełny tekst źródłaDissertação (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
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.
Pełny tekst źródłaBanca: 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
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/.
Pełny tekst źródłaColor 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.
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.
Pełny tekst źródłaLeite, 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.
Pełny tekst źródłaDissertação (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
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.
Pełny tekst źródłaAzzabou, Noura. "Variable Bandwidth Image Models for Texture-Preserving Enhancement of Natural Images". Paris Est, 2008. http://pastel.paristech.org/4041/01/ThesisNouraAzzabou.pdf.
Pełny tekst źródłaThis 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
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.
Pełny tekst źródłaTitle from document title page. Document formatted into pages; contains vii, 59 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 55-59).
Green, Lori Anne. "Tiled texture synthesis". Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/429.
Pełny tekst źródłaLladó, Bardera Xavier. "Texture recognition under varying imaging geometries". Doctoral thesis, Universitat de Girona, 2004. http://hdl.handle.net/10803/7721.
Pełny tekst źródłaLa 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.
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.
Pełny tekst źródłaBravo, 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.
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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
Muñoz, Pujol Xavier 1976. "Image segmentation integrating colour, texture and boundary information". Doctoral thesis, Universitat de Girona, 2003. http://hdl.handle.net/10803/7719.
Pełny tekst źródłaSe 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.
Linnett, L. M. "Multi-texture image segmentation". Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.
Pełny tekst źródłaMuhammad, Imran. "Colorizing Grey Scale Images". Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6181.
Pełny tekst źródłaIngratta, 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.
Pełny tekst źródłaHao, Chuan Yan. "Image completion based on texture regularity and texture synthesis". Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1940411.
Pełny tekst źródłaRajagopal, S. (Satish). "3D texture reconstruction from multi-view images". Master's thesis, University of Oulu, 2017. http://urn.fi/URN:NBN:fi:oulu-201706022492.
Pełny tekst źródłaMontoya, 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.
Pełny tekst źródłaDissertação (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
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/.
Pełny tekst źródłaMagnetic 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.
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.
Pełny tekst źródłaEl 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.
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.
Pełny tekst źródłaPetroudi, Styliani. "Texture in mammographic image analysis". Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422668.
Pełny tekst źródłaByrne, James. "Texture synthesis for image compression". Thesis, University of Bristol, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574259.
Pełny tekst źródłaGirometti, Laura. "Automatic texture-cartoon image decomposition". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24486/.
Pełny tekst źródłaCastelano, 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/.
Pełny tekst źródłaThis 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.
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.
Pełny tekst źródłaVieira, Fábio Henrique Antunes [UNESP]. "Image processing through machine learning for wood quality classification". Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/142813.
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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.
Ha, Minh Thien. "Modelling of stochastic and quasi-periodic texture images /". [S.l.] : [s.n.], 1989. http://library.epfl.ch/theses/?nr=804.
Pełny tekst źródłaSong, Keng Yew. "Surface defect detection on textured background". Thesis, University of Surrey, 1993. http://epubs.surrey.ac.uk/844113/.
Pełny tekst źródłaGalerne, Bruno. "Stochastic image models and texture synthesis". Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00595283.
Pełny tekst źródłaTan, Tieniu. "Image texture analysis : classification and segmentation". Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.
Pełny tekst źródłaChen, Yan Qiu. "Novel techniques for image texture classification". Thesis, University of Southampton, 1995. https://eprints.soton.ac.uk/250162/.
Pełny tekst źródłaAlaei, Fahimeh. "Texture Feature-based Document Image Retrieval". Thesis, Griffith University, 2019. http://hdl.handle.net/10072/385939.
Pełny tekst źródłaThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Tania, Sheikh. "Efficient texture descriptors for image segmentation". Thesis, Federation University Australia, 2022. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184087.
Pełny tekst źródłaDoctor of Philosophy
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.
Pełny tekst źródłaKlimeš, 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.
Pełny tekst źródłaSilva, 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/.
Pełny tekst źródłaPattern 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.
Camilleri, Kenneth P. "Multiresolution texture segmentation". Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.
Pełny tekst źródła