A Novel Algebaric Variety Based Model for High Quality Free-Viewpoint View Synthesis on a Krylov Subspace

Abstract: Graph Theory Concepts and Definitions Used in Image Processing and Analysis, O. Lezoray and L. Grady Introduction Basic Graph Theory Graph Representation Paths, Trees, and Connectivity Graph Models in Image Processing and Analysis Graph Cuts-Combinatorial Optimization in Vision, H. Ishikawa Introduction Markov Random Field Basic Graph Cuts: Binary Labels Multi-Label Minimization Examples Higher-Order Models in Computer Vision, P. Kohli and C. Rother Introduction Higher-Order Random Fields Patch and Region-Based Potentials Relating Appearance Models and Region-Based Potentials Global Potentials Maximum a Posteriori Inference A Parametric Maximum Flow Approach for Discrete Total Variation Regularization, A. Chambolle and J. Darbon Introduction Idea of the approach Numerical Computations Applications Targeted Image Segmentation Using Graph Methods, L. Grady The Regularization of Targeted Image Segmentation Target Specification Conclusion A Short Tour of Mathematical Morphology on Edge and Vertex Weighted Graphs, L. Najman and F. Meyer Introduction Graphs and lattices Neighborhood Operations on Graphs Filters Connected Operators and Filtering with the Component Tree Watershed Cuts MSF Cut Hierarchy and Saliency Maps Optimization and the Power Watershed Partial Difference Equations on Graphs for Local and Nonlocal Image Processing, A. Elmoataz, O. Lezoray, V.-T. Ta, and S. Bougleux Introduction Difference Operators on Weighted Graphs Construction of Weighted Graphs p-Laplacian Regularization on Graphs Examples Image Denoising with Nonlocal Spectral Graph Wavelets, D.K. Hammond, L. Jacques, and P. Vandergheynst Introduction Spectral Graph Wavelet Transform Nonlocal Image Graph Hybrid Local/Nonlocal Image Graph Scaled Laplacian Model Applications to Image Denoising Conclusions Acknowledgments Image and Video Matting, J. Wang Introduction Graph Construction for Image Matting Solving Image Matting Graphs Data Set Video Matting Optimal Simultaneous Multisurface and Multiobject Image Segmentation, X. Wu, M.K. Garvin, and M. Sonka Introduction Motivation and Problem Description Methods for Graph-Based Image Segmentation Case Studies Conclusion Acknowledgments Hierarchical Graph Encodings, L. Brun and W. Kropatsch Introduction Regular Pyramids Irregular Pyramids Parallel construction schemes Irregular Pyramids and Image properties Graph-Based Dimensionality Reduction, J.A. Lee and M. Verleysen Summary Introduction Classical methods Nonlinearity through Graphs Graph-Based Distances Graph-Based Similarities Graph embedding Examples and comparisons Graph Edit Distance-Theory, Algorithms, and Applications, M. Ferrer and H. Bunke Introduction Definitions and Graph Matching Theoretical Aspects of GED GED Computation Applications of GED The Role of Graphs in Matching Shapes and in Categorization, B. Kimia Introduction Using Shock Graphs for Shape Matching Using Proximity Graphs for Categorization Conclusion Acknowledgment 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching, A. Sharma, R. Horaud, and D. Mateus Introduction Graph Matrices Spectral Graph Isomorphism Graph Embedding and Dimensionality Reduction Spectral Shape Matching Experiments and Results Discussion Appendix: Permutation and Doubly- stochastic Matrices Appendix: The Frobenius Norm Appendix: Spectral Properties of the Normalized Laplacian Modeling Images with Undirected Graphical Models, M.F. Tappen Introduction Background Graphical Models for Modeling Image Patches Pixel-Based Graphical Models Inference in Graphical Models Learning in Undirected Graphical Models Tree-Walk Kernels for Computer Vision, Z. Harchaoui and F. Bach Introduction Tree-Walk Kernels as Graph Kernels The Region Adjacency Graph Kernel as a Tree-Walk Kernel The Point Cloud Kernel as a Tree-Walk Kernel Experimental Results Conclusion Acknowledgments

Abstract: The ability to interactively control viewpoint while watching a video is an exciting application of image-based rendering. The goal of our work is to render dynamic scenes with interactive viewpoint control using a relatively small number of video cameras. In this paper, we show how high-quality video-based rendering of dynamic scenes can be accomplished using multiple synchronized video streams combined with novel image-based modeling and rendering algorithms. Once these video streams have been processed, we can synthesize any intermediate view between cameras at any time, with the potential for space-time manipulation.In our approach, we first use a novel color segmentation-based stereo algorithm to generate high-quality photoconsistent correspondences across all camera views. Mattes for areas near depth discontinuities are then automatically extracted to reduce artifacts during view synthesis. Finally, a novel temporal two-layer compressed representation that handles matting is developed for rendering at interactive rates.

Abstract: Stereo correspondence methods rely on matching costs for computing the similarity of image locations. In this paper we evaluate the insensitivity of different matching costs with respect to radiometric variations of the input images. We consider both pixel-based and window-based variants and measure their performance in the presence of global intensity changes (e.g., due to gain and exposure differences), local intensity changes (e.g., due to vignetting, non-Lambertian surfaces, and varying lighting), and noise. Using existing stereo datasets with ground-truth disparities as well as six new datasets taken under controlled changes of exposure and lighting, we evaluate the different costs with a local, a semi-global, and a global stereo method.

Abstract: Image inpainting involves filling in part of an image or video using information from the surrounding area. Applications include the restoration of damaged photographs and movies and the removal of selected objects. We introduce a class of automated methods for digital inpainting. The approach uses ideas from classical fluid dynamics to propagate isophote lines continuously from the exterior into the region to be inpainted. The main idea is to think of the image intensity as a 'stream function for a two-dimensional incompressible flow. The Laplacian of the image intensity plays the role of the vorticity of the fluid; it is transported into the region to be inpainted by a vector field defined by the stream function. The resulting algorithm is designed to continue isophotes while matching gradient vectors at the boundary of the inpainting region. The method is directly based on the Navier-Stokes equations for fluid dynamics, which has the immediate advantage of well-developed theoretical and numerical results. This is a new approach for introducing ideas from computational fluid dynamics into problems in computer vision and image analysis.

Abstract: The problem of minimizing the rank of a matrix subject to affine constraints has applications in several areas including machine learning, and is known to be NP-hard. A tractable relaxation for this problem is nuclear norm (or trace norm) minimization, which is guaranteed to find the minimum rank matrix under suitable assumptions. In this paper, we propose a family of Iterative Reweighted Least Squares algorithms IRLS-p (with 0 ≤ p ≤ 1), as a computationally efficient way to improve over the performance of nuclear norm minimization. The algorithms can be viewed as (locally) minimizing certain smooth approximations to the rank function. When p = 1, we give theoretical guarantees similar to those for nuclear norm minimization, that is, recovery of low-rank matrices under certain assumptions on the operator defining the constraints. For p < 1, IRLS-p shows better empirical performance in terms of recovering low-rank matrices than nuclear norm minimization. We provide an efficient implementation for IRLS-p, and also present a related family of algorithms, sIRLS-p. These algorithms exhibit competitive run times and improved recovery when compared to existing algorithms for random instances of the matrix completion problem, as well as on the MovieLens movie recommendation data set.

Abstract: In this paper, we propose a new method of depth-image-based rendering (DIBR) for free-viewpoint TV (FTV). In the conventional method, we estimated the depth of an object on the virtual image plane, which is called view-dependent depth estimation, and the virtual view images are rendered using the view-dependent depth map. In this method, virtual viewpoint images are rendered with 3D warping instead of estimating the view-dependent depth, since depth estimation is usually costly and it is desirable to eliminate it from the rendering process. However, 3D warping causes some problems that do not occur in the method with view-dependent depth estimation; for example, the appearance of holes on the rendered image, and the occurrence of depth discontinuity on the surface of the object at virtual image plane. Depth discontinuity causes artifacts on the rendered image. In this paper, these problems are solved by projecting depth map to the virtual image plane and performing post-filtering on the projected depth map. In the experiments, high-quality arbitrary viewpoint images were obtained by rendering images from relatively small number of cameras.