Appearance Modelling For 4d Representations

Capturing spatio-temporal models (4D modelling) from real world imagery has received a growing interest during the last years urged by the increasing demands of real-world applications and the tremendous amount of easily accessible image data. The general objective is to produce realistic representations of the world from captured video sequences. Although geometric modelling has already reached a high level of maturity, the appearance aspect has not been fully explored. The current thesis addresses the problem of appearance modelling for realistic spatio-temporal representations. We propose a view-independent, high resolution appearance representation that successfully encodes the high visual variability of objects under various movements.First, we introduce a common appearance space to express all the available visual information from the captured images. In this space we define the representation of the global appearance of the subject. We then introduce a linear image formation model to simulate the capturing process and to express the multi-camera observations as different realizations of the common appearance. Identifying that the principle of Super-Resolution technique governs also our multi-view scenario, we extend the image generative model to accommodate it. In our work, we use Bayesian inference to solve for the super-resolved common appearance.Second, we propose a temporally coherent appearance representation. We extend the image formation model to generateimages of the subject captured in a small time interval. Our starting point is the observation thatthe appearance of the subject does not change dramatically in a predefined small time interval and the visual information from each view and each frame corresponds to the same appearance representation.We use Bayesian inference to exploit the visual redundant as well as the hidden non-redundant information across time, in order to obtain an appearance representation with fine details.Third, we leverage the interdependency of geometry and photometry and use it toestimate appearance and geometry in a joint manner. We show that by jointly estimating both, we are able to enhance the geometry globally that in turn leads to a significant appearance improvement.Finally, to further encode the dynamic appearance variability of objects that undergo several movements, we cast the appearance modelling as a dimensionality reduction problem. We propose a view-independent representation which builds on PCA and decomposesthe underlying appearance variability into Eigen textures and Eigen warps. The proposed framework is shown to accurately reproduce appearances with compact representations and to resolve appearance interpolation and completion tasks.

"Algorithms for scene understanding and realistic image synthesis require accurate models of the way real-world materials scatter light. This study describes recent work in the graphics community to measure the spatially- and directionally-varying reflectance and subsurface scattering of complex materials, and to develop efficient representations and analysis tools for these datasets. We describe the design of acquisition devices and capture strategies for reflectance functions such as BRDFs and BSSRDFs, efficient factored representations, and a case study of capturing the appearance of human faces"--Abstract.

Welcome to the proceedings of the 8th European Conference on Computer - sion! Following a very successful ECCV 2002, the response to our call for papers was almost equally strong – 555 papers were submitted. We accepted 41 papers for oral and 149 papers for poster presentation. Several innovations were introduced into the review process. First, the n- ber of program committee members was increased to reduce their review load. We managed to assign to program committee members no more than 12 papers. Second, we adopted a paper ranking system. Program committee members were asked to rank all the papers assigned to them, even those that were reviewed by additional reviewers. Third, we allowed authors to respond to the reviews consolidated in a discussion involving the area chair and the reviewers. Fourth, thereports,thereviews,andtheresponsesweremadeavailabletotheauthorsas well as to the program committee members. Our aim was to provide the authors with maximal feedback and to let the program committee members know how authors reacted to their reviews and how their reviews were or were not re?ected in the ?nal decision. Finally, we reduced the length of reviewed papers from 15 to 12 pages. ThepreparationofECCV2004wentsmoothlythankstothee?ortsofthe- ganizing committee, the area chairs, the program committee, and the reviewers. We are indebted to Anders Heyden, Mads Nielsen, and Henrik J. Nielsen for passing on ECCV traditions and to Dominique Asselineau from ENST/TSI who kindly provided his GestRFIA conference software. We thank Jan-Olof Eklundh and Andrew Zisserman for encouraging us to organize ECCV 2004 in Prague.

Create Genuine Visual Realism in Computer Graphics Digital Representations of the Real World: How to Capture, Model, and Render Visual Reality explains how to portray visual worlds with a high degree of realism using the latest video acquisition technology, computer graphics methods, and computer vision algorithms. It explores the integration of ne

Statistical shape and texture appearance models are powerful image representations, but previously had been restricted to 2D or 3D shapes with smooth surfaces and lambertian reflectance. In this thesis we present a novel 4D appearance model using image-based rendering techniques, which can represent complex lighting conditions, structures, and surfaces. We construct a light field manifold capturing the multi-view appearance of an object class and extend previous direct search algorithms to match new light fields or 2D images of an object to a point on this manifold. When matching to a 2D image the reconstructed light field can be used to render unseen views of the object. Our technique differs from previous view-based active appearance models in that model coefficients between views are explicitly linked, and that we do not model any pose variation within the deformable model at a single view. It overcomes the limitations of polygonal based appearance models and uses light fields that are acquired in real-time.

This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Features: with a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includes an extensive glossary; discusses the problems associated with detecting and tracking people through camera networks; examines topics related to determining the time-varying 3D pose of a person from video; investigates the representation and recognition of human and vehicular actions; reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance.

The three-volume set LNCS 7510, 7511, and 7512 constitutes the refereed proceedings of the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, held in Nice, France, in October 2012. Based on rigorous peer reviews, the program committee carefully selected 252 revised papers from 781 submissions for presentation in three volumes. The third volume includes 79 papers organized in topical sections on diffusion imaging: from acquisition to tractography; image acquisition, segmentation and recognition; image registration; neuroimage analysis; analysis of microscopic and optical images; image segmentation; diffusion weighted imaging; computer-aided diagnosis and planning; and microscopic image analysis.

Image-Based Computational Modeling of the Human Circulatory and Pulmonary Systems provides an overview of the current modeling methods and applications enhancing interventional treatments and computer-aided surgery. A detailed description of the techniques behind image acquisition, processing and three-dimensional reconstruction are included. Techniques for the computational simulation of solid and fluid mechanics and structure interaction are also discussed, in addition to various cardiovascular and pulmonary applications. Engineers and researchers involved with image processing and computational modeling of human organ systems will find this a valuable reference.

Abstract Biological vision is a rather fascinating domain of research. Scientists of various origins like biology, medicine, neurophysiology, engineering, math ematics, etc. aim to understand the processes leading to visual perception process and at reproducing such systems. Understanding the environment is most of the time done through visual perception which appears to be one of the most fundamental sensory abilities in humans and therefore a significant amount of research effort has been dedicated towards modelling and repro ducing human visual abilities. Mathematical methods play a central role in this endeavour. Introduction David Marr's theory v^as a pioneering step tov^ards understanding visual percep tion. In his view human vision was based on a complete surface reconstruction of the environment that was then used to address visual subtasks. This approach was proven to be insufficient by neuro-biologists and complementary ideas from statistical pattern recognition and artificial intelligence were introduced to bet ter address the visual perception problem. In this framework visual perception is represented by a set of actions and rules connecting these actions. The emerg ing concept of active vision consists of a selective visual perception paradigm that is basically equivalent to recovering from the environment the minimal piece information required to address a particular task of interest.