Energy Minimization Methods in Computer Vision and Pattern Recognition

Energy Minimization Methods in Computer Vision and Pattern Recognition : 5th International Workshop, EMMCVPR 2005, St. Augustine, FL, USA, November 9-11, 2005, Proceedings

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This volume consists of the 42 papers presented at the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recog- tion (EMMCVPR 2005), which was held at the Hilton St. Augustine Historic Bayfront,St. Augustine, Florida,USA, during November9-11,2005.This wo- shop is the ?fth in a series which began with EMMCVPR 1997 held in Venice, Italy, in May 1997 and continued with EMMCVPR 1999 held in York, UK, in July 1999, EMMCVPR 2001 held in Sophia-Antipolis, France, in September 2001 and EMMCVPR 2003 held in Lisbon, Portugal, in July 2003. Many problems in computer vision and pattern recognition (CVPR) are couchedintheframeworkofoptimization.Theminimizationofaglobalquantity, often referred to as the energy, forms the bulwark of most approachesin CVPR. Disparate approaches such as discrete and probabilistic formulations on the one hand and continuous, deterministic strategies on the other often have optimi- tion or energy minimization as a common theme.
Instances of energy minimi- tion arise in Gibbs/Markov modeling, Bayesian decision theory, geometric and variational approaches and in areas in CVPR such as object recognition and - trieval, image segmentation, registration, reconstruction, classi?cation and data mining. The aim of this workshop was to bring together researchers with interests in thesedisparateareasofCVPRbutwithanunderlyingcommitmenttosomeform of not only energy minimization but global optimization in general. Although thesubjectistraditionallywellrepresentedinmajorinternationalconferenceson CVPR, recent advances-information geometry, Bayesian networks and gra- ical models, Markov chain Monte Carlo, graph algorithms, implicit methods in variational approaches and PDEs-deserve an informal and focused hearing in a workshop setting.
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Product details

  • Paperback | 666 pages
  • 155 x 233 x 24.64mm | 2,080g
  • Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Berlin, Germany
  • English
  • 2005 ed.
  • XII, 666 p.
  • 3540302875
  • 9783540302872

Table of contents

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, Non-parametric 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.- Maximum-Likelihood Estimation of Biological Growth Variables.- Deformable-Model 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 Study.- 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 Cospectrality of Graphs and Trees.- Variational Approaches.- Geodesic Image Matching: A Wavelet Based Energy Minimization Scheme.- Geodesic Shooting and Diffeomorphic Matching Via Textured Meshes.- An Adaptive Variational Model for Image Decomposition.- Segmentation Informed by Manifold Learning.- One-Shot Integral Invariant Shape Priors for Variational Segmentation.- Dynamic Shape and Appearance Modeling Via Moving and Deforming Layers.- Energy Minimization Based Segmentation and Denoising Using a Multilayer Level Set Approach.- Constrained Total Variation Minimization and Application in Computerized Tomography.- Some New Results on Non-rigid Correspondence and Classification of Curves.- Edge Strength Functions as Shape Priors in Image Segmentation.- Spatio-temporal Prior Shape Constraint for Level Set Segmentation.- A New Implicit Method for Surface Segmentation by Minimal Paths: Applications in 3D Medical Images.- Other Approaches and Applications.- Increasing Efficiency of SVM by Adaptively Penalizing Outliers.- Locally Linear Isometric Parameterization.- A Constrained Hybrid Optimization Algorithm for Morphable Appearance Models.- Kernel Methods for Nonlinear Discriminative Data Analysis.- Reverse-Convex Programming for Sparse Image Codes.- Stereo for Slanted Surfaces: First Order Disparities and Normal Consistency.- Brain Image Analysis Using Spherical Splines.- High-Order Differential Geometry of Curves for Multiview Reconstruction and Matching.
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