Bibliografie tematiche / Textured images

Letteratura scientifica selezionata sul tema "Textured images"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Textured images":

Oliveira, Miguel, Gi-Hyun Lim, Tiago Madeira, Paulo Dias e Vítor Santos. "Robust Texture Mapping Using RGB-D Cameras". Sensors 21, n. 9 (7 maggio 2021): 3248. http://dx.doi.org/10.3390/s21093248.

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The creation of a textured 3D mesh from a set of RGD-D images often results in textured meshes that yield unappealing visual artifacts. The main cause is the misalignments between the RGB-D images due to inaccurate camera pose estimations. While there are many works that focus on improving those estimates, the fact is that this is a cumbersome problem, in particular due to the accumulation of pose estimation errors. In this work, we conjecture that camera poses estimation methodologies will always display non-neglectable errors. Hence, the need for more robust texture mapping methodologies, capable of producing quality textures even in considerable camera misalignments scenarios. To this end, we argue that use of the depth data from RGB-D images can be an invaluable help to confer such robustness to the texture mapping process. Results show that the complete texture mapping procedure proposed in this paper is able to significantly improve the quality of the produced textured 3D meshes.

Hemalatha, S., e S. Margret Anouncia. "A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection". International Journal of Ambient Computing and Intelligence 7, n. 2 (luglio 2016): 93–113. http://dx.doi.org/10.4018/ijaci.2016070105.

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This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that texture feature is analogous to edges and thus, the time complexity is reduced significantly. The model proposed in this work is based on Gaussian kernel smoothing, Fractional partial derivatives and a statistical approach. It is justified to be robust to noisy images and possesses statistical interpretation. The model is validated by the classification experiments on different types of textured images from Brodatz album. It achieves higher classification accuracy than the existing methods.

Dal’Col, Lucas, Daniel Coelho, Tiago Madeira, Paulo Dias e Miguel Oliveira. "A Sequential Color Correction Approach for Texture Mapping of 3D Meshes". Sensors 23, n. 2 (5 gennaio 2023): 607. http://dx.doi.org/10.3390/s23020607.

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Texture mapping can be defined as the colorization of a 3D mesh using one or multiple images. In the case of multiple images, this process often results in textured meshes with unappealing visual artifacts, known as texture seams, caused by the lack of color similarity between the images. The main goal of this work is to create textured meshes free of texture seams by color correcting all the images used. We propose a novel color-correction approach, called sequential pairwise color correction, capable of color correcting multiple images from the same scene, using a pairwise-based method. This approach consists of sequentially color correcting each image of the set with respect to a reference image, following color-correction paths computed from a weighted graph. The color-correction algorithm is integrated with a texture-mapping pipeline that receives uncorrected images, a 3D mesh, and point clouds as inputs, producing color-corrected images and a textured mesh as outputs. Results show that the proposed approach outperforms several state-of-the-art color-correction algorithms, both in qualitative and quantitative evaluations. The approach eliminates most texture seams, significantly increasing the visual quality of the textured meshes.

Bhaumik, Shubrajit, Viorel Paleu, Dhrubajyoti Chowdhury, Adarsh Batham, Udit Sehgal, Basudev Bhattacharya, Chiradeep Ghosh e Shubhabrata Datta. "Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions". Materials 15, n. 23 (27 novembre 2022): 8445. http://dx.doi.org/10.3390/ma15238445.

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The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.

Coelho, Daniel, Lucas Dal’Col, Tiago Madeira, Paulo Dias e Miguel Oliveira. "A Robust 3D-Based Color Correction Approach for Texture Mapping Applications". Sensors 22, n. 5 (23 febbraio 2022): 1730. http://dx.doi.org/10.3390/s22051730.

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Texture mapping of 3D models using multiple images often results in textured meshes with unappealing visual artifacts known as texture seams. These artifacts can be more or less visible, depending on the color similarity between the used images. The main goal of this work is to produce textured meshes free of texture seams through a process of color correcting all images of the scene. To accomplish this goal, we propose two contributions to the state-of-the-art of color correction: a pairwise-based methodology, capable of color correcting multiple images from the same scene; the application of 3D information from the scene, namely meshes and point clouds, to build a filtering procedure, in order to produce a more reliable spatial registration between images, thereby increasing the robustness of the color correction procedure. We also present a texture mapping pipeline that receives uncorrected images, an untextured mesh, and point clouds as inputs, producing a final textured mesh and color corrected images as output. Results include a comparison with four other color correction approaches. These show that the proposed approach outperforms all others, both in qualitative and quantitative metrics. The proposed approach enhances the visual quality of textured meshes by eliminating most of the texture seams.

Akl, Adib. "Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images". Cybernetics and Information Technologies 18, n. 1 (1 marzo 2018): 51–68. http://dx.doi.org/10.2478/cait-2018-0005.

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Abstract This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive semi-definite matrices is used to estimate the structural information represented by the local orientation and the degree of anisotropy in structured and sinusoid-like textured images. In order to ensure faithful local structure estimation, an adaptive algorithm for the regularization of the extent of gradient fields smoothing is proposed. Results obtained on different texture samples show the strength of the proposed method in accurately representing the local variation of orientations in the underlying textured images, which paves the way towards an accurate analysis of the texture structures.

Beschastnov, Nikolay P., Irina V. Rybaulina e Evdokia N. Dergileva. "FACTURE, TEXTURE AND TEHNO-ORNAMENT IN MODERN DESIGN: FUNCTION AND ARTISTIC MEANING". Technologies & Quality 51, n. 1 (29 aprile 2021): 40–45. http://dx.doi.org/10.34216/2587-6147-2021-1-51-40-45.

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The article is devoted to defining the place and role of textured formations and techno-ornament in modern design, setting out the features of their use and methods of obtaining. The sources of the artistic attitude to the texture and texture of the material in the creation of works of decorative and applied art, interiors are briefly outlined, the importance of increased attention to them in the modern period is revealed. A special role is assigned to techno-ornamentation, which has arisen in high-tech culture and has become an exponent of new rhythmic-plastic images that are in tune with modernity. Images with machine aesthetics with alternating elements that do not have open semantic content. The authors came to the conclusion that in the late 20 th to the early 21 st centuries there was an unprecedented increase in interest in textured and textural ornamental formations. Their images are very vague and associated with an environment largely distanced from humans. Ornament in such an environment is extremely contextual.

Barburiceanu, Stefania, Romulus Terebes e Serban Meza. "3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors". Applied Sciences 11, n. 5 (5 marzo 2021): 2332. http://dx.doi.org/10.3390/app11052332.

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Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

Wen, Mingyun, Jisun Park e Kyungeun Cho. "Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks". Remote Sensing 13, n. 21 (22 ottobre 2021): 4254. http://dx.doi.org/10.3390/rs13214254.

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This study focuses on reconstructing accurate meshes with high-resolution textures from single images. The reconstruction process involves two networks: a mesh-reconstruction network and a texture-reconstruction network. The mesh-reconstruction network estimates a deformation map, which is used to deform a template mesh to the shape of the target object in the input image, and a low-resolution texture. We propose reconstructing a mesh with a high-resolution texture by enhancing the low-resolution texture through use of the super-resolution method. The architecture of the texture-reconstruction network is like that of a generative adversarial network comprising a generator and a discriminator. During the training of the texture-reconstruction network, the discriminator must focus on learning high-quality texture predictions and to ignore the difference between the generated mesh and the actual mesh. To achieve this objective, we used meshes reconstructed using the mesh-reconstruction network and textures generated through inverse rendering to generate pseudo-ground-truth images. We conducted experiments using the 3D-Future dataset, and the results prove that our proposed approach can be used to generate improved three-dimensional (3D) textured meshes compared to existing methods, both quantitatively and qualitatively. Additionally, through our proposed approach, the texture of the output image is significantly improved.

Gimel'Farb, Georgy L., e Anil K. Jain. "On retrieving textured images from an image database". Pattern Recognition 29, n. 9 (settembre 1996): 1461–83. http://dx.doi.org/10.1016/0031-3203(96)00011-8.

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Più fonti

Tesi sul tema "Textured images":

Li, Zhongqiang. "Segmentation of textured images". Thesis, University of Central Lancashire, 1991. http://clok.uclan.ac.uk/20270/.

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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.

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.

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Noriega, Leonardo Antonio. "The colorimetric segmentation of textured digital images". Thesis, Southampton Solent University, 1998. http://ssudl.solent.ac.uk/2444/.

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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.

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.

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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.

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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.

Văcar, Cornelia Paula. "Inversion for textured images : unsupervised myopic deconvolution, model selection, deconvolution-segmentation". Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0131/document.

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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 convergence
This 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.

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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.

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.

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Achddou, Raphaël. "Synthetic learning for neural image restoration methods". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT006.

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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 naturelles
Photography 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.

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Made available in DSpace on 2014-06-11T19:26:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-25Bitstream added on 2014-06-13T19:06:36Z : No. of bitstreams: 1 casaca_wco_me_sjrp.pdf: 5215634 bytes, checksum: 291e2a21fdb4d46a11de22f18cc97f93 (MD5)
Fundação 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.

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Libri sul tema "Textured images":

Hung, Chih-Cheng, Enmin Song e Yihua Lan. Image Texture Analysis. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13773-1.

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Harris, David Earl. Texture analysis of skin cancer images. Ann Arbor, Mich: UMI, 1991.

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Stoichita, Victor. Les Fileuses de Velazquez: Textes, textures, images. Paris]: Fayard, 2018.

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Gimel’farb, Georgy L. Image Textures and Gibbs Random Fields. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4461-2.

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Gimel'farb, Georgy L. Image Textures and Gibbs Random Fields. Dordrecht: Springer Netherlands, 1999.

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Gimelʹfarb, Georgiĭ Lʹvovich. Image textures and Gibbs random fields. Dordrecht: Kluwer Academic Publishers, 1999.

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Wood, E. J. Carpet texture measurement using image analysis. Christchurch: Wronz, 1987.

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Spann, Michael. Texture description and segmentation in image processing. Birmingham: University of Aston. Department of Electrical and Electronic Engineering, 1985.

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Chaki, Jyotismita, e Nilanjan Dey. Texture Feature Extraction Techniques for Image Recognition. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0853-0.

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Rand, Robert S. Texture analysis and cartographic feature extraction. Fort Belvoir, Va: U.S. Army Corps of Engineers, Engineer Topographic Laboratories, 1985.

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Capitoli di libri sul tema "Textured images":

Rouquet, Catherine, e Pierre Bonton. "Region-based segmentation of textured images". In Image Analysis and Processing, 11–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_230.

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Mičušík, Branislav, e Allan Hanbury. "Steerable Semi-automatic Segmentation of Textured Images". In Image Analysis, 35–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_5.

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Santos, Roi, Xosé R. Fdez-Vidal e Xosé M. Pardo. "Adaptive Line Matching for Low-Textured Images". In Pattern Recognition and Image Analysis, 192–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_22.

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Casadei, Stefano, Sanjoy Mitter e Pietro Perona. "Boundary detection in piecewise homogeneous textured images". In Computer Vision — ECCV'92, 174–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55426-2_20.

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Kjell, Bradley P., e Charles R. Dyer. "Segmentation of Textured Images by Pyramid Linking". In Pyramidal Systems for Computer Vision, 273–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-82940-6_17.

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Manfredi, Guido, Michel Devy e Daniel Sidobre. "Textured Object Recognition: Balancing Model Robustness and Complexity". In Computer Analysis of Images and Patterns, 52–63. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_5.

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Grau, Antoni, e Jordi Saludes. "Improved textured images segmentation using an energy functional". In Image Analysis and Processing, 70–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_186.

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Azencott, R., C. Graffrigne e C. Labourdette. "Edge Detection and Segmentation of Textured Plane Images". In Stochastic Models, Statistical Methods, and Algorithms in Image Analysis, 75–88. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2920-9_4.

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Taha, Bilal, Munawar Hayat, Stefano Berretti e Naoufel Werghi. "Fused Geometry Augmented Images for Analyzing Textured Mesh". In Lecture Notes in Computer Science, 3–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54407-2_1.

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Lee, Yun-Seok, Seung-Hun Yoo e Chang-Sung Jeong. "Modified Hough Transform for Images Containing Many Textured Regions". In Rough Sets and Current Trends in Computing, 824–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11908029_85.

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Atti di convegni sul tema "Textured images":

Sanzharov, Vadim Vladimirovich, e Vladimir Alexandrovich Frolov. "Viewpoint Selection for Texture Reconstruction with Inverse Rendering". In 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-66-77.

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Abstract (sommario):

Viewpoint selection methods have a variety of applications in different fields of computer graphics and computer vision, including shape retrieval, scientific visualization, image-based modeling and others. In this paper we investigate the applicability of existing viewpoint selection methods to the problem of textures reconstruction using inverse rendering. First, we use forward rendering to produce path-traced images of a textured object. Then we apply different view quality metrics to select a set of images for texture reconstruction. Finally, we perform material and texture reconstruction using these image sets and evaluate the quality of the results. We show that using viewpoint selection methods allows to achieve faster inverse rendering times while maintaining quality of the results.

Turner, Mark R. "Gabor functions and textural segmentation". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wj38.

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This paper investigates the applicability of Gabor functions to textural segmentation. Gabor functions are sinusoidal plane waves in 2-D Gaussian envelopes. The choice of parameters characterizing the geometry of an individual Gabor function affects its spatial extent as well as orientation and spatial frequency tuning. Daugman has indicated that these functions belong to a class of filters having optimal joint resolution in the 2-D space and 2-D frequency domains. They are, therefore, appropriate filter choices for tasks which require selective measurement in these domains. Textural segmentation appears to be one of those tasks. A set of Gabor functions of different frequencies and orientations is applied by computer program to images containing regions of different texture. This process produces a kind of localized and orientation selective frequency spectrum of various fields in the image. The program then attempts to delineate the boundaries of the textured regions by identifying spectrum differences between these fields. Gabor functions are effective in distinguishing between many of the textures used in psychophysical studies differing in first- or second-order statistics. Additional textures in which the difference is related to some aspect of the collinearity of the texture elements have also been tried with promising results.

Weber, Allan G., e Alexander A. Sawchuk. "Segmentation of Textured Images". In Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.fb1.

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A requirement of any vision system is to segment the image into regions having a set of common characteristics. In many applications, this common characteristic is texture. Texture is a higher order image property, and depends on the statistics of pixels in a local neighborhood. To perform segmentation, the regions of homogeneous higher order statistics must be identified and the spatial boundaries where statistical properties change must be located. These local statistics are derived from measurements within a window whose dimensions are subject to a conflicting set of requirements. Using a spatially large window produces better statistical accuracy in homogenous regions having stationary statistics, but it averages over boundaries between regions of different texture, leading to many incorrectly segmented pixels. Using a spatially small averaging window produces more accurate classifications near texture boundaries, but has poor statistical accuracy compared to the large windows. The optimum window sizes to be used are not known a priori, thus we use a hierarchy of averaging windows having a range of sizes and resolutions. The overall goal is to improve the overall segmentation quality over that obtained at a single resolution.

WU, DONG-SHENG, LE-NAN WU e BO HUANG. "AUTOMATION TEXTURED AND NON-TEXTURED IMAGES CLASSIFICATION AND RETRIEVAL". In Proceedings of the Second International Conference. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704313_0050.

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"COLOR AND TEXTURE BASED SEGMENTATION ALGORITHM FOR MULTICOLOR TEXTURED IMAGES". In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002042502580263.

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Zeng, Yu, e Biyu Wan. "Saliency Detection in Textured Images". In 2020 15th International Conference on Computer Science & Education (ICCSE). IEEE, 2020. http://dx.doi.org/10.1109/iccse49874.2020.9201616.

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He, Qiang, e Chee-Hung Henry Chu. "Shadow removal from textured images". In SPIE Defense, Security, and Sensing, a cura di Daniel J. Henry. SPIE, 2009. http://dx.doi.org/10.1117/12.818983.

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Giovannelli, Jean-Francois, e Cornelia Vacar. "Deconvolution-segmentation for textured images". In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081195.

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Dolez, Benoit, e Nicole Vincent. "Sample Selection in Textured Images". In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379132.

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Turtinen, M., e M. Pietikainen. "Contextual Analysis of Textured Scene Images". In British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.87.

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Rapporti di organizzazioni sul tema "Textured images":

Alhasan, Ahmad, Brian Moon, Doug Steele, Hyung Lee e Abu Sufian. Chip Seal Quality Assurance Using Percent Embedment. Illinois Center for Transportation, dicembre 2023. http://dx.doi.org/10.36501/0197-9191/23-029.

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This study investigates the use of macrotexture as an indicator of the percent embedment (PE) of aggregate in a chip seal and ultimately, as a quality assurance tool for chip seals. The study included an extensive field- and controlled-testing program from 24 chip seal sections constructed in Illinois. Surface texture measurements were acquired using a high-speed texture profiler and a stationary laser texture device. The analysis showed that stationary texture measurements were more consistent and reliable for estimating PE and characterizing chip seals in the field. Moreover, the ground truth PE values were estimated using an image analysis algorithm implemented on side-view images of cores extracted in the field. The ground truth PE values were estimated using four approaches: the average elevation method, percent embedment of each aggregate method, the peak method, and the aggregate circumference method. The analysis showed that the correlations between the different PE estimation methods are relatively weak, indicating the various methods provide different information and may relate to different characteristics. The general regression models for PE values estimated using the average elevation method and the mean profile depth (MPD) acquired using laser texture scans and the average least dimension (ALD) yielded the highest R2 value of 0.50. The model showed a consistent decreasing trend between PE and MPD estimated using laser texture scans and side-view images. Moreover, the model matched the expected behavior that PE should reach 100% as MPD reaches 0. Finally, four models were recommended correlating PE estimated using the average elevation and each aggregate methods to the MPD (mm) estimated from laser texture scans and ALD (mm) estimated from side-view images.

McKay, Paul, e C. A. Blain. An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2013. http://dx.doi.org/10.21236/ada609737.

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LaCascia, Marco, John Isidoro e Stan Sclaroff. Head Tracking via Robust Registration in Texture Map Images. Fort Belvoir, VA: Defense Technical Information Center, agosto 1998. http://dx.doi.org/10.21236/ada366993.

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Carasso, Alfred S. Singular integrals, image smoothness, and the recovery of texture in image deblurring. Gaithersburg, MD: National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.ir.7005.

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Wendelberger, James G. Localized Similar Image Texture in Images of Sample Laser Confocal Microscope for Area: FY15 DE07 SW C1 Zone 1 & 2 Section b. Office of Scientific and Technical Information (OSTI), febbraio 2019. http://dx.doi.org/10.2172/1496724.

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Gletsos, M., S. G. Mougiakakou, G. K. Matsopoulos, K. S. Nikita e D. Kelekis. Classification of Hepatic Lesions From CT Images Using Texture Features and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, ottobre 2001. http://dx.doi.org/10.21236/ada412422.

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Rosenblum, W. I., C. Salvaggio e J. R. Schott. Selection of optimal textural features for maximum likelihood image classification. Office of Scientific and Technical Information (OSTI), gennaio 1990. http://dx.doi.org/10.2172/5098367.

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Du, Li-Jen. Segmentation of Synthetic Aperture Radar (SAR) Images of Ocean Surface by the Texture Energy Transform Method. Fort Belvoir, VA: Defense Technical Information Center, agosto 1988. http://dx.doi.org/10.21236/ada199536.

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Pe-Piper, G., D. J W Piper, J. Nagle e P. Opra. Petrography of bedrock and ice-rafted granules: Flemish Cap, offshore Newfoundland and Labrador. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331224.

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Abstract (sommario):

This Open File report provides petrographic information from a scanning electron microscope study of granules and small pebbles in four selected trawl samples from Flemish Cap. The mineral composition of the granules was determined by energy dispersive spectroscopy (EDS) and textures are shown in backscattered electron images (BSE). It complements Open File 8359 on the heavy mineral assemblage on Flemish Cap. Granules on the central shoals appear to be derived from outcropping Avalonian basement; those to the east and west are predominantly ice-rafted in origin. These data improve our understanding of the source of the voluminous sands on Flemish Cap and the characteristics of the Avalonian basement rocks on southern Flemish Cap.

Andrawes, Bassem, Ernesto Perez Claros e Zige Zhang. Bond Characteristics and Experimental Behavior of Textured Epoxy-coated Rebars Used in Concrete Bridge Decks. Illinois Center for Transportation, gennaio 2022. http://dx.doi.org/10.36501/0197-9191/22-001.

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The deterioration of bridge decks is a problem typically associated with the corrosion of the reinforcing steel. This issue was partially controlled during the 1970s with the incorporation of the epoxy-coating protection system. However, research later demonstrated that the smooth surface resulting from the epoxy-coating application reduces most of the friction between the rebar and the surrounding concrete. Consequently, forces acting on the rib faces are reconfigured in such a way that the radial components increase, triggering the early development of cracks. To mitigate both the reduction of bonding and the formation of cracks, the Illinois Department of Transportation proposed a new type of coated bars: textured epoxy-coated (TEC) bars. Over the last few years, different projects have been executed to understand and improve the characteristics of TEC rebars. This report is a continuation of research performed at the University of Illinois Urbana-Champaign to evaluate the bond behavior of TEC bars. The experimental program starts by characterizing, qualitatively and quantitatively, the roughness of the TEC rebars. Next, their bond-slip interaction embedded in concrete is evaluated through pull-out tests. Finite element models of these tests are developed to validate the behavior observed as the textured reinforcement loses anchorage with concrete. Based on these results, the experimental program then aims to study the impact of the drying shrinkage, temperature change, and flexural demands on two large-scale bridge deck specimens reinforced, individually, with TEC and standard epoxy-coated bars. The results collected from both specimens using digital image correlation and strain gauges are compared to explore the differences exhibited by the traditional and the new type of reinforcement coatings in terms of stress distribution in bridge decks. Finally, given the specialized equipment and time-consuming procedure needed to calculate the roughness parameters of TEC bars, an empirical, weight-based approach is developed as a rapid method for assessing the rebars’ roughness on-site.