Thermal evaporator was used to coat a layer of silver nano-particles and thin film on (100) plane of rock salt . The experiment content can be divide into three parts . In the first part , the (100) texture of Ag nano-film and the evolution of the microstructure during annealing were studied . Little change in microstructure was found during low temperature annealing . In the second part , the salt surface was treated with deionized water and chlorine to try to align the Ag nano-particles on its surface . The effect of annealing on the texture and the defects of the Ag nano-particles were analyzed . The stacking fault was found to form early in the nucleation stage . In the third part , Ag nano-particles were scattered on the (100) texture , Ag nano-film and their rotation was studied by annealing at different temperature . Most Ag nano-particles were confirmed to rotate to a parallel orientation relationship with the Ag nano-film .

La géométrie fractale, comprenant les analyses fractales et multifractales, est un outil en émergence dans de nombreux domaines d’applications et notamment en imagerie médicale. Elle consiste à formuler une mesure de l’hétérogénéité globale ou locale d’un signal (1D, 2D ou 3D). En imagerie médicale, son utilisation est souvent limitée au cas 1D ou 2D et ses domaines d’applications restent essentiellement restreints à la discrimination entre deux états (sains/pathologiques) grâce à une analyse globale du signal. L’objectif de cette thèse est de fournir à la fois des outils 3D de mesures des hétérogénéités globale (volume) et locale (voxel) basées sur la géométrie fractale. Les deux indices utilisés sont la dimension fractale et le spectre multifractal (coefficients de Hölder). Etant donné que les algorithmes de ces outils ne peuvent formuler que des estimations de la valeur théorique, nous utilisons des volumes de synthèses fractals et multifractals pour les valider. Les différents développements offrent ainsi non seulement des outils de discrimination mais également des outils de segmentation de texture. Ce deuxième point est particulièrement intéressant, car le développement de nouveaux attributs de texture est un domaine de recherche actif du fait de l’évolution incessante des technologies d’imagerie. Afin de valider et de montrer l’intérêt de la géométrie fractale, deux applications sont étudiées : la caractérisation des foyers épileptogènes sur des images de tomographie par émission mono-photonique, et la détection des tumeurs prostatiques sur des images IRM pondérées T2. L’efficacité des attributs fractals et multifractals sont étudiés à travers un schéma de classification supervisée. Les résultats concluants pour les deux applications démontrent l’intérêt de cette géométrie et son adaptabilité à différentes applications en imagerie médicale. / Fractal geometry is an emerging concept used in medical image analysis. The aim of this geometry is to measure global and local heterogeneities (1D, 2D or 3D). In medical imaging, it is often used to characterize 1D and 2D signals and restricted to discriminate between 2 states (healthy/pathological) by a global analysis of a signal. This thesis aims at providing 3D fractal geometry based tools for the measures of global (volume) and local (voxel) heterogeneities. Two indices are used: fractal dimension and multifractal spectrum (Hölder exponents). Since these algorithms estimate the theoretical value, fractal and multifractal synthetic volumes were used for the validation. This work also proposes texture segmentation tools. Two applications were studied; characterization of epileptic foci on single photon emission computed tomography images and the detection of prostatic tumors on T2-weighted MR images. The effectiveness of fractal and multifractal features are studied through a framework of supervised classification. The results for both applications demonstrate the usefulness of this geometry and its adaptability to several applications in medical imaging.

Can texture embody the idea of a place? Or does a place provide meaning to the textures used to construct it? This thesis seeks to explore the contribution of texture to our understanding of, and relationship with, the built environment. To test this, a mixed use program will be explored in the neighborhood of Mount Vernon Square in Washington, DC. / Master of Architecture

Texture segmentation is an effortless process in scene analysis, yet its mechanisms have not been sufficiently understood. Several theories and algorithms exist for texture discrimination based on vision. These models diverge from one another in algorithmic approaches to address texture imagery using spatial elements and their statistics. Even though there are differences among these approaches, they all begin from the assumption that texture segmentation is a visual task. However, considering that texture is basically a surface property, this assumption can at times be misleading. An interesting possibility is that since surface properties are most immediately accessible to touch, texture perception may be more intimately associated with texture than with vision (it is known that tactile input can affect vision). Coincidentally, the basic organization of the touch (somatosensory) system bears some analogy to that of the visual system. In particular, recent neurophysiological findings showed that receptive fields for touch resemble that of vision, albeit with some subtle differences. The main novelty and contribution of this thesis is in the use of tactile receptive field responses for texture segmentation. Furthermore, we showed that touch-based representation is superior to its vision-based counterpart when used in texture boundary detection. Tactile representations were also found to be more discriminable (LDA and ANOVA). We expect our results to help better understand the nature of texture perception and build more powerful texture processing algorithms. The results suggest that touch has an advantage over vision in texture processing. Findings in this study are expected to shed new light on the role of tactile perception of texture and its interaction with vision, and help develop more powerful, biologically inspired texture segmentation algorithms.

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Hardware texture mapping is essential for real-time rendering. Unfortunately the memory bandwidth and latency often bounds performance in current graphics architectures. Bandwidth consumption can be reduced by compressing the texture map or by using a cache. However, the way a texture map occupies memory and how it is accessed affects the pattern of memory accesses, which in turn affects cache performance. Thus texture compression schemes and cache architectures must be designed in conjunction with each other. We define a sparse texture to be a texture where a substantial percentage of the texture is constant. Sparse textures are of interest as they occur often, and they are used as parts of more general texture compression schemes. We present a hardware compatible implementation of sparse textures based on B-tree indexing and explore cache designs for it. We demonstrate that it is possible to have the bandwidth consumption and miss rate due to the texture data alone scale with the area of the region of interest. We also show that the additional bandwidth consumption and hideable latency due to the B-tree indices are low. Furthermore, the caches necessary for these textures can be quite small.

A regular texture is formed from a regular congruent tiling of perceptually meaningful texture elements, also known as textons. If the tiling statistically deviates from regularity, either by texton structure, colour, or size, the texture is called near-regular. If we continue to perturb the tiling, the texture becomes stochastic. The set of possible textures that lie between regular and stochastic make up the texture spectrum: regular, near-regular, regular, near-stochastic, and stochastic. <p>In this thesis we provide a solution to the problem of creating, from a near-regular texture, a lattice which defines the placement of textons. We divide the problem into two distinct sub-areas: finding textons within an image, and lattice creation using both an ad-hoc method and a graph-theoretic method. <p>The problem of finding textons within an image is addressed using correlation. A texton selected by the user is correlated with the image and points of high correlation are extracted using non-maximal suppression. To extend this framework to irregular textures, we present early results on the use of feature space during correlation. We also present a method of correcting for a specific type of error in the texton finding result using frequency-space analysis. <p>Given texton locations, we provide two methods of creating a lattice. The ad-hoc method is able to create a lattice in spite of inconsistencies in the texton locating data. However, as texture becomes irregular the ad-hoc lattice construction method fails to correctly connect textons. To overcome this failure we adapt methods of creating proximity graphs, which join two textons whose neighbourhoods satisfy certain criteria, to our problem. The proximity graphs are parameterized for selection of the most appropriate graph choice for a given texture, solving the general lattice construction problem given correct texton locations. <p>In the output of the algorithm, centres of textons will be connected by edges in the lattice following the structure of texton placement within the input image. More precisely, for a texture T, we create a graph G = (V,E) dependent on T, where V is a set of texton centres, and E ={(v_i, v_j)} is a set of edges, where v_i, v_j are in V. Each edge e in E connects texton centre v in V to its most perceptually sensible neighbours.

Hardware texture mapping is essential for real-time rendering. Unfortunately the memory bandwidth and latency often bounds performance in current graphics architectures. Bandwidth consumption can be reduced by compressing the texture map or by using a cache. However, the way a texture map occupies memory and how it is accessed affects the pattern of memory accesses, which in turn affects cache performance. Thus texture compression schemes and cache architectures must be designed in conjunction with each other. We define a sparse texture to be a texture where a substantial percentage of the texture is constant. Sparse textures are of interest as they occur often, and they are used as parts of more general texture compression schemes. We present a hardware compatible implementation of sparse textures based on B-tree indexing and explore cache designs for it. We demonstrate that it is possible to have the bandwidth consumption and miss rate due to the texture data alone scale with the area of the region of interest. We also show that the additional bandwidth consumption and hideable latency due to the B-tree indices are low. Furthermore, the caches necessary for these textures can be quite small.

A regular texture is formed from a regular congruent tiling of perceptually meaningful texture elements, also known as textons. If the tiling statistically deviates from regularity, either by texton structure, colour, or size, the texture is called near-regular. If we continue to perturb the tiling, the texture becomes stochastic. The set of possible textures that lie between regular and stochastic make up the texture spectrum: regular, near-regular, regular, near-stochastic, and stochastic. <p>In this thesis we provide a solution to the problem of creating, from a near-regular texture, a lattice which defines the placement of textons. We divide the problem into two distinct sub-areas: finding textons within an image, and lattice creation using both an ad-hoc method and a graph-theoretic method. <p>The problem of finding textons within an image is addressed using correlation. A texton selected by the user is correlated with the image and points of high correlation are extracted using non-maximal suppression. To extend this framework to irregular textures, we present early results on the use of feature space during correlation. We also present a method of correcting for a specific type of error in the texton finding result using frequency-space analysis. <p>Given texton locations, we provide two methods of creating a lattice. The ad-hoc method is able to create a lattice in spite of inconsistencies in the texton locating data. However, as texture becomes irregular the ad-hoc lattice construction method fails to correctly connect textons. To overcome this failure we adapt methods of creating proximity graphs, which join two textons whose neighbourhoods satisfy certain criteria, to our problem. The proximity graphs are parameterized for selection of the most appropriate graph choice for a given texture, solving the general lattice construction problem given correct texton locations. <p>In the output of the algorithm, centres of textons will be connected by edges in the lattice following the structure of texton placement within the input image. More precisely, for a texture T, we create a graph G = (V,E) dependent on T, where V is a set of texton centres, and E ={(v_i, v_j)} is a set of edges, where v_i, v_j are in V. Each edge e in E connects texton centre v in V to its most perceptually sensible neighbours.