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Cover Art
E-RESOURCE
Author Escolano, Francisco.

Title Information theory in computer vision and pattern recognition [electronic resource] / Francisco Escolano, Pablo Suau, Boyán Bonev ; foreword by Alan Yuille.

Published London : Springer, c2009.

Copies

Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Physical description 1 online resource (1 v.) : ill.
Bibliography Includes bibliographical references and index.
Contents Cover -- Contents -- 1 Introduction -- 1.1 Measures, Principles, Theories, and More -- 1.2 Detailed Organization of the Book -- 1.3 The ITinCVPR Roadmap -- 2 Interest Points, Edges, and Contour Grouping -- 2.1 Introduction -- 2.2 Entropy and Interest Points -- 2.2.1 Kadir and Brady Scale Saliency Detector -- 2.2.2 Point Filtering by Entropy Analysis Through Scale Space -- 2.2.3 Chernoff Information and Optimal Filtering -- 2.2.4 Bayesian Filtering of the Scale Saliency Feature Extractor: The Algorithm -- 2.3 Information Theory as Evaluation Tool: The Statistical Edge Detection Case -- 2.3.1 Statistical Edge Detection -- 2.3.2 Edge Localization -- 2.4 Finding Contours Among Clutter -- 2.4.1 Problem Statement -- 2.4.2 AddXMLRootTags.pl Road Tracking -- 2.4.3 AddXMLRootTags.pl Convergence Proof -- 2.5 Junction Detection and Grouping -- 2.5.1 Junction Detection -- 2.5.2 Connecting and Filtering Junctions -- Problems -- 2.6 Key References -- 3 Contour and Region-Based Image Segmentation -- 3.1 Introduction -- 3.2 Discriminative Segmentation with JensenShannon Divergence -- 3.2.1 The Active Polygons Functional -- 3.2.2 JensenShannon Divergence and the Speed Function -- 3.3 MDL in Contour-Based Segmentation -- 3.3.1 B-Spline Parameterization of Contours -- 3.3.2 MDL for B-Spline Parameterization -- 3.3.3 MDL Contour-based Segmentation -- 3.4 Model Order Selection in Region-Based Segmentation -- 3.4.1 Jump-Diffusion for Optimal Segmentation -- 3.4.2 Speeding-up the Jump-Diffusion Process -- 3.4.3 K-adventurers Algorithm -- 3.5 Model-Based Segmentation Exploiting The Maximum Entropy Principle -- 3.5.1 Maximum Entropy and Markov Random Fields -- 3.5.2 Efficient Learning with Belief Propagation -- 3.6 Integrating Segmentation, Detection and Recognition -- 3.6.1 Image Parsing -- 3.6.2 The Data-Driven Generative Model -- 3.6.3 The Power of Discriminative Processes -- 3.6.4 The Usefulness of Combining Generative and Discriminative -- Problems -- 3.7 Key References -- 4 Registration, Matching, and Recognition -- 4.1 Introduction -- 4.2 Image Alignment and Mutual Information -- 4.2.1 Alignment and Image Statistics -- 4.2.2 Entropy Estimation with Parzens Windows -- 4.2.3 The EMMA Algorithm -- 4.2.4 Solving the Histogram-Binning Problem -- 4.3 Alternative Metrics for Image Alignment -- 4.3.1 Normalizing Mutual Information -- 4.3.2 Conditional Entropies -- 4.3.3 Extension to the Multimodal Case -- 4.3.4 Affine Alignment of Multiple Images -- 4.3.5 The R233;nyi Entropy -- 4.3.6 R233;nyis Entropy and Entropic Spanning Graphs -- 4.3.7 The JensenR233;nyi Divergence and Its Applications -- 4.3.8 Other Measures Related to R233;nyi Entropy -- 4.3.9 Experimental Results -- 4.4 Deformable Matching with Jensen Divergence and Fisher Information -- 4.4.1 The Distributional Shape Model -- 4.4.2 Multiple Registration and JensenShannon Divergence -- 4.4.3 Information Geometry and FisherRao Information -- 4.4.4 Dynamics of the Fisher Information Metric -- 4.5 Structural Learning with MDL -- 4.5.1 The Usefulness of Shock Trees -- 4.5.2 A Generative Tree Model Based on Mixtures -- 4.5.3 Learning the Mixture -- 4.5.4 Tree Edit-Distance and MDL -- Problems -- 4.6 Key References -- 5 Image and Pattern Clustering -- 5.1 Introduction -- 5.2 Gaussian Mixtures and Model Selection -- 5.2.1 Gaussian Mixtu
Notes Description based on print version record.
Other author Suau, Pablo.
Bonev, Boyán.
Subject Information theory.
Computer vision.
Pattern perception.
Electronic books.
ISBN 9781848822979
1848822979
9786612332197
6612332190