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Textured motion - generally known as dynamic or temporal texture - is a popular research area for synthesis, segmentation and recognition. Dynamic texture is a spatially repetitive, time-varying visual pattern that forms an image sequence with certain temporal stationarity. In dynamic texture, the notion of self-similarity central to conventional image texture is extended to the spatiotemporal domain. Dynamic textures are typically videos of processes, such as waves, smoke, fire, a flag blowing in the wind, a moving escalator, or a walking crowd. Creation of synthetic frames is a key issue especially for movie screen industry to enrich their scenes from a white screen into a shining reality. In robotics world, for example an autonomous vehicle must decide what is traversable terrain (e.g. grass) and what is not (e.g. water). This problem can be addressed by classifying portions of the image into a number of categories, for instance grass, dirt, bushes or water. If these parts are identifiable, then segmentation and recognition of these textures results with an efficient path planning for the autonomous vehicle. In this thesis, we aimed to characterize these textured motions like mentioned above. We tried to implement several known techniques and compared the results.

Cette thèse s’inscrit dans le contexte de la synthèse d’images texturées. Dans l’objectif d’assurer une reproduction fidèle des motifs et des variations d’orientations d’une texture initiale, un algorithme de synthèse de texture à deux étapes « structure/texture » est proposé. Il s’agit, dans une première étape, de réaliser la synthèse d’une couche de structure caractérisant la géométrie de l’exemplaire et représentée par un champ de tenseurs de structure et, dans une deuxième étape, d’utiliser le champ de structure résultant pour contraindre la synthèse d’une couche de texture portant des variations plus locales. Une réduction du temps d’exécution est ensuite développée, fondée notamment sur l’utilisation de pyramides Gaussiennes et la parallélisation des calculs mis en oeuvre.Afin de démontrer la capacité de l’algorithme proposé à reproduire fidèlement l’aspect visuel des images texturées considérées, la méthode est testée sur une variété d’échantillons de texture et évaluée objectivement à l’aide de statistiques du 1er et du 2nd ordre du champ d’intensité et d’orientation. Les résultats obtenus sont de qualité supérieure ou équivalente à ceux obtenus par des algorithmes de la littérature. Un atout majeur de l’approche proposée est son aptitude à synthétiser des textures avec succès dans de nombreuses situations où les algorithmes existants ne parviennent pas à reproduire les motifs à grande échelle.L’approche de synthèse structure/texture proposée est étendue à la synthèse de texture couleur. La synthèse de texture 3D est ensuite abordée et, finalement, une extension à la synthèse de texture de forme spécifiée par une texture imposée est mise en oeuvre, montrant la capacité de l’approche à générer des textures de formes arbitraires en préservant les caractéristiques de la texture initiale. / This work is a part of the texture synthesis context. Aiming to ensure a faithful reproduction of the patterns and variations of orientations of the input texture, a two-stage structure/texture synthesis algorithm is proposed. It consists of synthesizing the structure layer showing the geometry of the exemplar and represented by the structure tensor field in the first stage, and using the resulting tensor field to constrain the synthesis of the texture layer holding more local variations, in the second stage. An acceleration method based on the use of Gaussian pyramids and parallel computing is then developed.In order to demonstrate the ability of the proposed algorithm to faithfully reproduce the visual aspect of the considered textures, the method is tested on various texture samples and evaluated objectively using statistics of 1st and 2nd order of the intensity and orientation field. The obtained results are of better or equivalent quality than those obtained using the algorithms of the literature. A major advantage of the proposed approach is its capacity in successfully synthesizing textures in many situations where traditional algorithms fail to reproduce the large-scale patterns.The structure/texture synthesis approach is extended to color texture synthesis. 3D texture synthesis is then addressed and finally, an extension to the synthesis of specified form textures using an imposed texture is carried out, showing the capacity of the approach in generating textures of arbitrary forms while preserving the input texture characteristics.

Texture boundary detection (or segmentation) is an important capability of the human visual system. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct surfaces or objects, thus, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this thesis, I investigated the relative difficulty of learning to segment textures in 2D vs. 3D configurations. It turns out that learning is faster and more accurate in 3D, very much in line with what was expected. Furthermore, I have shown that the learned ability to segment texture in 3D transfers well into 2D texture segmentation, but not the other way around, bolstering the initial hypothesis, and providing an alternative approach to the texture segmentation problem.

The purpose of this thesis is to develop a working methodology to color a grey scale image. This thesis is based on approach of using a colored reference image. Coloring grey scale images has no exact solution till date and all available methods are based on approximation. This technique of using a color reference image for approximating color information in grey scale image is among most modern techniques.Method developed here in this paper is better than existing methods of approximation of color information addition in grey scale images in brightness, sharpness, color shade gradients and distribution of colors over objects.Color and grey scale images are analyzed for statistical and textural features. This analysis is done only on basis of luminance value in images. These features are then segmented and segments of color and grey scale images are mapped on basis of distances of segments from origin. Then chromatic values are transferred between these matched segments from color image to grey scale image.Technique proposed in this paper uses better mechanism of mapping clusters and mapping colors between segments, resulting in notable improvement in existing techniques in this category.

Texture boundary detection (or segmentation) is an important capability of the human visual system. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct surfaces or objects, thus, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this thesis, I investigated the relative difficulty of learning to segment textures in 2D vs. 3D configurations. It turns out that learning is faster and more accurate in 3D, very much in line with what was expected. Furthermore, I have shown that the learned ability to segment texture in 3D transfers well into 2D texture segmentation, but not the other way around, bolstering the initial hypothesis, and providing an alternative approach to the texture segmentation problem.

Traditionally, human texture perception has been studied using artificial textures made of random-dot patterns or abstract structured elements. At the same time, computer algorithms for the synthesis of natural textures have improved dramatically. The current study seeks to unify these two fields of research through a psychophysical assessment of a particular computational model, thus providing a sense of what image statistics are most vital for representing a range of natural textures. We employ Portilla and Simoncelli’s 2000 model of texture synthesis for this task (a parametric model of analysis and synthesis designed to mimic computations carried out by the human visual system). We find an intriguing interaction between texture type (periodic v. structured) and image statistics (autocorrelation function and filter magnitude correlations), suggesting different processing strategies may be employed for these two texture families under pre-attentive viewing.

Cette thèse étudie l'effet de la ségrégation des solutés d’étain sur la formation de la texture de recristallisation dans les alliages ferritiques. La diffraction d’électrons rétrodiffusés (EBSD) et la sonde atomique tomographique ont été utilisées pour étudier respectivement le développement de la texture et la ségrégation locale des atomes de soluté. Des mesures d’EBSD in situ révèlent que l'hétérogénéité de la déformation dans la microstructure laminée à froid est un facteur crucial pour l’évolution au cours du recuit ultérieur, en particulier dans les alliages ternaires Fe-Si-Sn. L’ajout d’étain s'est avéré avoir un effet profond sur la texture de recuit. Il a été montré que Sn affecte principalement les phénomènes de recuit par interaction soluté-dislocation et ségrégation aux joints de grains. Des observations directes par sonde atomique tomographique à chaque étape de la recristallisation est discuté et un effet fort au stade de la germination de la recristallisation est mis en évidence. La sonde atomique tomographique combinée à la modélisation atomistique de la ségrégation à l’équilibre a permis de conclure que la ségrégation dépend de la désorientation. Néanmoins, la ségrégation du soluté dans les joints de grains à grand angle (joints de grains spéciaux et généraux) s'est avérée indépendante de leurs caractéristiques géométriques. Enfin, le développement de la texture peut s’expliquer par la théorie de la nucléation orientée de la recristallisation, alors que la présence de certaines interfaces mobiles pourrait également contribuer à la croissance orientée de certains grains recristallisés / This Ph.D. thesis investigates the effect of Sn solute segregation on the formation of recrystallisation texture in ferritic alloys. Both electron back-scatter diffraction and atom probe tomography were used to investigate the texture development and the local solute segregation respectively. In-situ electron back-scatter diffraction reveals that the strain heterogeneity in the deformed microstructure is a crucial factor for subsequent annealing, especially in the solute added alloys. Solute was found to have a profound effect on the annealing texture. Mainly, Sn was shown to affect the annealing phenomena via solute-dislocation interaction and grain boundary segregation. Direct observations with atom probe tomography reveal and quantify the levels of segregation at grain boundaries during the development of the recrystallised microstructure. The role of segregation at each stage of recrystallisation is discussed and a strong effect at the recrystallisation nucleation stage is suggested. Atom probe tomography results in combination with atomistic modelling of equilibrium segregation, concluded that the segregation depends on the misorientation. Nonetheless, the solute segregation in high-angle grain boundaries was found to be independent of their geometric characteristics (i.e. general, special grain boundaries). Finally, texture development could be explained in terms of the oriented nucleation theory of recrystallisation, while the presence of some mobile interfaces may subsequently also contribute in the oriented growth of some recrystallised grains

Texture Segmentation is one of the most challenging areas of computer vision. One reason for this difficulty is the huge variety and variability of textures occurring in real world, making it very difficult to quantitatively study textures. One of the key tools used for texture segmentation is local invariant descriptors. Texture consists of textons, the basic building block of textures, that may vary by small nuisances like illumination variation, deformations, and noise. Local invariant descriptors are robust to these nuisances making them beneficial for texture segmentation. However, grouping dense descriptors directly for segmentation presents a problem: existing descriptors aggregate data from neighborhoods that may contain different textured regions, making descriptors from these neighborhoods difficult to group, leading to significant errors in segmentation. This work addresses this issue by proposing dense local descriptors, called Shape-Tailored Features, which are tailored to an arbitrarily shaped region, aggregating data only within the region of interest. Since the segmentation, i.e., the regions, are not known a-priori, we propose a joint problem for Shape-Tailored Features and the regions. We present a framework based on variational methods. Extensive experiments on a new large texture dataset, which we introduce, show that the joint approach with Shape-Tailored Features leads to better segmentations over the non-joint non Shape-Tailored approach, and the method out-performs existing state-of-the-art.

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