Non-parametric synthesis of laminar volumetric texture

International audienceThe goal of this paper is to evaluate several extensions of Wei and Levoy's algorithm for the synthesis of laminar volumetric textures constrained only by a single 2D sample. Hence, we shall also review in a unified form the improved algorithm proposed by Kopf et al. and the particular histogram matching approach of Chen and Wang. Developing a genuine quantitative study we are able to compare the performances of these algorithms that we have applied to the synthesis of volumetric structures of dense carbons. The 2D samples are lattice fringe images obtained by high resolution transmission electronic microscopy (HRTEM)

Markov-Gibbs Random Field Approach for Modeling of Skin Surface Textures

Medical imaging has been contributing to dermatology by providing computer-based assistance by 2D digital imaging of skin and processing of images. Skin imaging can be more effective by inclusion of 3D skin features. Furthermore, clinical examination of skin consists of both visual and tactile inspection. The tactile sensation is related to 3D surface profiles and mechanical parameters. The 3D imaging of skin can also be integrated with haptic technology for computer-based tactile inspection. The research objective of this work is to model 3D surface textures of skin. A 3D image acquisition set up capturing skin surface textures at high resolution (~0.1 mm) has been used. An algorithm to extract 2D grayscale texture (height map) from 3D texture has been presented. The extracted 2D textures are then modeled using Markov-Gibbs random field (MGRF) modeling technique. The modeling results for MGRF model depend on input texture characteristics. The homogeneous, spatially invariant texture patterns are modeled successfully. From the observation of skin samples, we classify three key features of3D skin profiles i.e. curvature of underlying limb, wrinkles/line like features and fine textures. The skin samples are distributed in three input sets to see the MGRF model's response to each of these 3D features. First set consists of all three features. Second set is obtained after elimination of curvature and contains both wrinkle/line like features and fine textures. Third set is also obtained after elimination of curvature but consists of fine textures only. MGRF modeling for set I did not result in any visual similarity. Hence the curvature of underlying limbs cannot be modeled successfully and makes an inhomogeneous feature. For set 2 the wrinkle/line like features can be modeled with low/medium visual similarity depending on the spatial invariance. The results for set 3 show that fine textures of skin are almost always modeled successfully with medium/high visual similarity and make a homogeneous feature. We conclude that the MGRF model is able to model fine textures of skin successfully which are on scale of~ 0.1 mm. The surface profiles on this resolution can provide haptic sensation of roughness and friction. Therefore fine textures can be an important clue to different skin conditions perceived through tactile inspection via a haptic device

Completing unknown portions of 3D scenes by 3D visual propagation

Institute of Perception, Action and BehaviourAs the requirement for more realistic 3D environments is pushed forward by the computer {graphics | movie | simulation | games} industry, attention turns away from the creation of purely synthetic, artist derived environments towards the use of real world captures from the 3D world in which we live. However, common 3D acquisition techniques, such as laser scanning and stereo capture, are realistically only 2.5D in nature - such that the backs and occluded portions of objects cannot be realised from a single uni-directional viewpoint. Although multi-directional capture has existed for sometime, this incurs additional temporal and computational cost with no existing guarantee that the resulting acquisition will be free of minor holes, missing surfaces and alike. Drawing inspiration from the study of human abilities in 3D visual completion, we consider the automated completion of these hidden or missing portions in 3D scenes originally acquired from 2.5D (or 3D) capture. We propose an approach based on the visual propagation of available scene knowledge from the known (visible) scene areas to these unknown (invisible) 3D regions (i.e. the completion of unknown volumes via visual propagation - the concept of volume completion). Our proposed approach uses a combination of global surface fitting, to derive an initial underlying geometric surface completion, together with a 3D extension of nonparametric texture synthesis in order to provide the propagation of localised structural 3D surface detail (i.e. surface relief). We further extend our technique both to the combined completion of 3D surface relief and colour and additionally to hierarchical surface completion that offers both improved structural results and computational efficiency gains over our initial non-hierarchical technique. To validate the success of these approaches we present the completion and extension of numerous 2.5D (and 3D) surface examples with relief ranging in natural, man-made, stochastic, regular and irregular forms. These results are evaluated both subjectively within our definition of plausible completion and quantitatively by statistical analysis in the geometric and colour domains

Synthesis and evaluation of geometric textures

Two-dimensional geometric textures are the geometric analogues of raster (pixel-based) textures and consist of planar distributions of discrete shapes with an inherent structure. These textures have many potential applications in art, computer graphics, and cartography. Synthesizing large textures by hand is generally a tedious task. In raster-based synthesis, many algorithms have been developed to limit the amount of manual effort required. These algorithms take in a small example as a reference and produce larger similar textures using a wide range of approaches. Recently, an increasing number of example-based geometric synthesis algorithms have been proposed. I refer to them in this dissertation as Geometric Texture Synthesis (GTS) algorithms. Analogous to their raster-based counterparts, GTS algorithms synthesize arrangements that ought to be judged by human viewers as “similar” to the example inputs. However, an absence of conventional evaluation procedures in current attempts demands an inquiry into the visual significance of synthesized results. In this dissertation, I present an investigation into GTS and report on my findings from three projects. I start by offering initial steps towards grounding texture synthesis techniques more firmly with our understanding of visual perception through two psychophysical studies. My observations throughout these studies result in important visual cues used by people when generating and/or comparing similarity of geometric arrangements as well a set of strategies adopted by participants when generating arrangements. Based on one of the generation strategies devised in these studies I develop a new geometric synthesis algorithm that uses a tile-based approach to generate arrangements. Textures synthesized by this algorithm are comparable to the state of the art in GTS and provide an additional reference in subsequent evaluations. To conduct effective evaluations of GTS, I start by collecting a set of representative examples, use them to acquire arrangements from multiple sources, and then gather them into a dataset that acts as a standard for the GTS research community. I then utilize this dataset in a second set of psychophysical studies that define an effective methodology for comparing current and future geometric synthesis algorithms

Dynamic texture synthesis in image and video processing.

Xu, Leilei.Thesis submitted in: October 2007.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 78-84).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Texture and Dynamic Textures --- p.1Chapter 1.2 --- Related work --- p.4Chapter 1.3 --- Thesis Outline --- p.7Chapter 2 --- Image/Video Processing --- p.8Chapter 2.1 --- Bayesian Analysis --- p.8Chapter 2.2 --- Markov Property --- p.10Chapter 2.3 --- Graph Cut --- p.12Chapter 2.4 --- Belief Propagation --- p.13Chapter 2.5 --- Expectation-Maximization --- p.15Chapter 2.6 --- Principle Component Analysis --- p.15Chapter 3 --- Linear Dynamic System --- p.17Chapter 3.1 --- System Model --- p.18Chapter 3.2 --- Degeneracy and Canonical Model Realization --- p.19Chapter 3.3 --- Learning of Dynamic Textures --- p.19Chapter 3.4 --- Synthesizing Dynamic Textures --- p.21Chapter 3.5 --- Summary --- p.21Chapter 4 --- Dynamic Color Texture Synthesis --- p.25Chapter 4.1 --- Related Work --- p.25Chapter 4.2 --- System Model --- p.26Chapter 4.2.1 --- Laplacian Pyramid-based DCTS Model --- p.28Chapter 4.2.2 --- RBF-based DCTS Model --- p.28Chapter 4.3 --- Experimental Results --- p.32Chapter 4.4 --- Summary --- p.42Chapter 5 --- Dynamic Textures using Multi-resolution Analysis --- p.43Chapter 5.1 --- System Model --- p.44Chapter 5.2 --- Multi-resolution Descriptors --- p.46Chapter 5.2.1 --- Laplacian Pyramids --- p.47Chapter 5.2.2 --- Haar Wavelets --- p.48Chapter 5.2.3 --- Steerable Pyramid --- p.49Chapter 5.3 --- Experimental Results --- p.51Chapter 5.4 --- Summary --- p.55Chapter 6 --- Motion Transfer --- p.59Chapter 6.1 --- Problem formulation --- p.60Chapter 6.1.1 --- Similarity on Appearance --- p.61Chapter 6.1.2 --- Similarity on Dynamic Behavior --- p.62Chapter 6.1.3 --- The Objective Function --- p.65Chapter 6.2 --- Further Work --- p.66Chapter 7 --- Conclusions --- p.67Chapter A --- List of Publications --- p.68Chapter B --- Degeneracy in LDS Model --- p.70Chapter B.l --- Equivalence Class --- p.70Chapter B.2 --- The Choice of the Matrix Q --- p.70Chapter B.3 --- Swapping the Column of C and A --- p.71Chapter C --- Probability Density Functions --- p.74Chapter C.1 --- Probability Distribution --- p.74Chapter C.2 --- Joint Probability Distributions --- p.75Bibliography --- p.7

Statistical Model-Based Corneal Reconstruction

Precise measurements of corneal layer thickness are required to treat, evaluate risk of, and determine the progression of pathologies within the eye. The thickness measurements are typically acquired as 2d images, known as tomograms, from an optical coherence tomography (OCT) system. With the creation of ultra-high resolution OCT (UHROCT), there is active research in precisely measuring, in vivo, previously unresolvable corneal structures at arbitrary locations within the cornea to determine their relationship with corneal health. In order to obtain arbitrary corneal thickness measurements, existing reconstruction techniques require the cornea to be densely sampled so that a 3d representation can be interpolated from a stack of tomograms. Unfortunately, tomogram alignment relies solely on image properties such as pixel intensity, and does not constrain the reconstruction to corneal anatomy. Further, the reconstruction method cannot properly compensate for eye-motion. The deficiencies due to eye-motion are exacerbated due to the amount of time required in a single imaging session to acquire a sufficient number of tomograms in the region of interest. The proposed methodology is the first to incorporate models of the anatomy and the imaging system to address the limitations of existing corneal reconstruction methods. By constructing the model in such a way as to decouple anatomy from the imaging system, it becomes less computationally expensive to estimate model parameters. The decoupling provides an iterative methodology that can allow additional constraints to be introduced in the future. By combining sparsely sampled UHROCT measurements with a properly designed corneal model, reconstruction allows researchers to determine corneal layer thicknesses at arbitrary positions in both sampled and unsampled regions. The proposed methodology demonstrates an approach to decouple anatomy and physiology from measurements of a cornea, allowing for characterization of pathologies through corneal thickness measurements. Another significant contribution resulting from the corneal model allows five of the corneal layer boundaries to be automatically located and has already been used to process thousands of UHROCT tomograms. Recent studies using this method have also been used to correlate contact-lens wear to hypoxia and corneal layer swelling. While corneal reconstruction represents the main application of this work, the reconstruction methodology can be extended to other medical imaging domains and can even represent temporal changes in tissue with minor modifications to the framework

Analyse / synthèse de champs de tenseurs de structure : application à la synthèse d’images et de volumes texturés

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.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

ICASE/LaRC Symposium on Visualizing Time-Varying Data

Time-varying datasets present difficult problems for both analysis and visualization. For example, the data may be terabytes in size, distributed across mass storage systems at several sites, with time scales ranging from femtoseconds to eons. In response to these challenges, ICASE and NASA Langley Research Center, in cooperation with ACM SIGGRAPH, organized the first symposium on visualizing time-varying data. The purpose was to bring the producers of time-varying data together with visualization specialists to assess open issues in the field, present new solutions, and encourage collaborative problem-solving. These proceedings contain the peer-reviewed papers which were presented at the symposium. They cover a broad range of topics, from methods for modeling and compressing data to systems for visualizing CFD simulations and World Wide Web traffic. Because the subject matter is inherently dynamic, a paper proceedings cannot adequately convey all aspects of the work. The accompanying video proceedings provide additional context for several of the papers