A Convergence Proof for the Horn-Schunck Optical-Flow Computation Scheme Using Neighborhood Decomposition
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چکیده
Scaling of Plane Figures That Assures Faithful Digitization p. 87 Computing Admissible Rotation Angles from Rotated Digital Images p. 99 On the Number of hv-Convex Discrete Sets p. 112 Finding the Orthogonal Hull of a Digital Object: A Combinatorial Approach p. 124 A Discrete Approach for Supervised Pattern Recognition p. 136 Image Representation, Segmentation, Grouping, and Reconstruction Robust Decomposition of Thick Digital Shapes p. 148 Segmentation of Noisy Discrete Surfaces p. 160 MRF Labeling with a Graph-Shifts Algorithm p. 172 Label Space: A Multi-object Shape Representation p. 185 A New Image Segmentation Technique Using Maximum Spanning Tree p. 197 Applications of Computational Geometry, Integer and Linear Programming to Image Analysis Reducing the Coefficients of a Two-Dimensional Integer Linear Constraint p. 205 A Branch & Bound Algorithm for Medical Image Registration p. 217 Global Optimization for First Order Markov Random Fields with Submodular Priors p. 229 Transformation Polytopes for Line Correspondences in Digital Images p. 238 Linear Boundary and Corner Detection Using Limited Number of Sensor Rows p. 250 Fuzzy and Stochastic Image Analysis, Parallel Architectures and Algorithms A Convergence Proof for the Horn-Schunck Optical-Flow Computation Scheme Using Neighborhood Decomposition p. 262
منابع مشابه
A Proof of Convergence of the Horn-Schunck Optical Flow Algorithm in Arbitrary Dimension
The Horn and Schunck (HS) method, which amounts to the Jacobi iterative scheme in the interior of the image, was one of the first optical flow algorithms. In this article, we prove the convergence of the HS method, whenever the problem is well-posed. Our result is shown in the framework of a generalization of the HS method in dimension n ≥ 1, with a broad definition of the discrete Laplacian. I...
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