Energy Minimization Methods in Computer Vision 计算机视觉与模式识别中的能量最小化方法
2005-12-20
Springer
Rangajaran, Anand; Vemuri, Baba; Yuille, Alan L.
666
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This book constitutes the refereed proceedings of the 5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2005, held in St. Augustine, FL, USA in November 2005. The 24 revised full papers and 18 poster papers presented were carefully reviewed and selected from 120 submissions. The papers are organized in topical sections on probabilistic and informational approaches, combinatorial approaches, variational approaches, and other approaches and applications.
I Probabilistic and Informational Approaches Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application A Computational Approach to Fisher Information Geometry with Applications to Image Analysis Optimizing the Cauchy Schwarz PDF Distance for Information Theoretic, Nonparametric Clustering Concurrent Stereo Matching: An Image Noise Driven Model Color Correction of Underwater Images for Aquatic Robot Inspection Bayesian Image Segmentation Using Gaussian Field Priors Handling Missing Data in the Computation of 3D Affine Transformations MaximumLikelihood Estimation of Biological Growth Variables DeformableModel Based Textured Object Segmentation Total Variation Minimization and a Class of Binary MRF Models Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical StudyII Combinatorial Approaches Probabilistic Subgraph Matching Based on Convex Relaxation Relaxation of Hard Classification Targets for LSE Minimization Linear Programming Matching and Appearance-Adaptive Object Tracking Extraction of Layers of Similar Motion Through Combinatorial Techniques Object Categorization by Compositional Graphical Models Learning Hierarchical Shape Models from Examples Discontinuity Preserving Phase Unwrapping Using Graph Cuts Retrieving Articulated 3-D Models Using Medial Surfaces and Their Graph Spectra Spatio-temporal Segmentation Using Dominant Sets Stable Bounded Canonical Sets and Image Matching Coined Quantum Walks Lift the Cospectraity of Graphs and TreesIII Variational AppraoachesIV Other Approaches and ApplicationsSubject InedxAuthor Index
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