Energy Minimization Methods in Computer Vision and Pattern Recognition de Edwin R. Hancock

Energy Minimization Methods in Computer Vision and Pattern Recognition: Second International Workshop, EMMCVPR'99, York, UK, July 26-29, 1999, Proceedings: Lecture Notes in Computer Science, cartea 1654

Edwin R. Hancock

Marcello Pelillo

en Limba Engleză Paperback – 14 iul 1999

This book constitutes the refereed proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'97, held in Venice, Italy, in May 1997.
The book presents 29 revised full papers selected from a total of 62 submissions. Also included are four full invited papers and a keynote paper by leading researchers. The volume is organized in sections on contours and deformable models, Markov random fields, deterministic methods, object recognition, evolutionary search, structural models, and applications. The volume is the first comprehensive documentation of the application of energy minimization techniques in the areas of compiler vision and pattern recognition.

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Paperback (2)	317^.28 lei 6-8 săpt.
Springer Berlin, Heidelberg – 14 iul 1999	317^.28 lei 6-8 săpt.
Springer Berlin, Heidelberg – 29 apr 1997	327^.61 lei 6-8 săpt.

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317^.28 lei 6-8 săpt.

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317^.28 lei 6-8 săpt.

Springer Berlin, Heidelberg – 29 apr 1997

327^.61 lei 6-8 săpt.

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Cuprins

Shape.- A Hamiltonian Approach to the Eikonal Equation.- Topographic Surface Structure from 2D Images Using Shape-from-Shading.- Harmonic Shape Images: A Representation for 3D Free-Form Surfaces Based on Energy Minimization.- Deformation Energy for Size Functions.- Minimum Description Length.- On Fitting Mixture Models.- Bayesian Models for Finding and Grouping Junctions.- Markov Random Fields.- Semi-iterative Inferences with Hierarchical Energy-Based Models for Image Analysis.- Metropolis vs Kawasaki Dynamic for Image Segmentation Based on Gibbs Models.- Hyperparameter Estimation for Satellite Image Restoration by a MCMCML Method.- Auxiliary Variables for Markov Random Fields with Higher Order Interactions.- Unsupervised Multispectral Image Segmentation Using Generalized Gaussian Noise Model.- Contours.- Adaptive Bayesian Contour Estimation: A Vector Space Representation Approach.- Adaptive Pixel-Based Data Fusion for Boundary Detection.- Search and Consistent Labeling.- Bayesian A* Tree Search with Expected O(N) Convergence Rates for Road Tracking.- A New Algorithm for Energy Minimization with Discontinuities.- Convergence of a Hill Climbing Genetic Algorithm for Graph Matching.- A New Distance Measure for Non-rigid Image Matching.- Continuous-Time Relaxation Labeling Processes.- Tracking and Video.- Realistic Animation Using Extended Adaptive Mesh for Model Based Coding.- Maximum Likelihood Inference of 3D Structure from Image Sequences.- Biomedical Applications.- Magnetic Resonance Imaging Based Correction and Reconstruction of Positron Emission Tomography Images.- Markov Random Field Modelling of fMRI Data Using a Mean Field EM-algorithm4.