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Produktbild: Energy Minimization Methods in Computer Vision and Pattern Recognition
Band 3757

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

Aus der Reihe Lecture Notes in Computer Science

97,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

31.10.2005

Abbildungen

XII, 666 p.

Herausgeber

Anand Rangarajan + weitere

Verlag

Springer Berlin

Seitenzahl

666

Maße (L/B/H)

23,5/15,5/3,7 cm

Gewicht

1013 g

Auflage

2005

Sprache

Englisch

ISBN

978-3-540-30287-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

31.10.2005

Abbildungen

XII, 666 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

666

Maße (L/B/H)

23,5/15,5/3,7 cm

Gewicht

1013 g

Auflage

2005

Sprache

Englisch

ISBN

978-3-540-30287-2

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Energy Minimization Methods in Computer Vision and Pattern Recognition
  • 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.