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Pattern theory : from representation to inference

Author: Ulf Grenander; Michael I Miller
Publisher: Oxford : Oxford University Press, 2007.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:

A comprehensive overview of the challenges in signal, data and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision. Includes numerous exercises, an  Read more...

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Genre/Form: Electronic books
Additional Physical Format: Print version:
Grenander, Ulf.
Pattern theory.
Oxford : Oxford University Press, 2007
(DLC) 2007274231
(OCoLC)65467603
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ulf Grenander; Michael I Miller
ISBN: 9780191523113 0191523119 1280753854 9781280753855 9781429470148 1429470143
OCLC Number: 137240084
Description: 1 online resource (xii, 596 pages) : illustrations (some color)
Contents: Contents; 1 Introduction; 2 The Bayes Paradigm, Estimation and Information Measures; 3 Probabilistic Directed Acyclic Graphs and Their Entropies; 4 Markov Random Fields on Undirected Graphs; 5 Gaussian Random Fields on Undirected Graphs; 6 The Canonical Representations of General Pattern Theory; 7 Matrix Group Actions Transforming Patterns; 8 Manifolds, Active Models, and Deformable Templates; 9 Second Order and Gaussian Fields; 10 Metrics Spaces for the Matrix Groups; 11 Metrics Spaces for the Infinite Dimensional Diffeomorphisms; 12 Metrics on Photometric and Geometric Deformable Templates. 13 Estimation Bounds for Automated Object Recognition14 Estimation on Metric Spaces with Photometric Variation; 15 Information Bounds for Automated Object Recognition; 16 Computational Anatomy: Shape, Growth and Atrophy Comparison via Diffeomorphisms; 17 Computational Anatomy: Hypothesis Testing on Disease; 18 Markov Processes and Random Sampling; 19 Jump Diffusion Inference in Complex Scenes; References; Index.
Responsibility: Ulf Grenander and Michael I. Miller.

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"Patterns Theory: From Representations to Inference" provides a comprehensive and accessible overview of the modern challenges in signal, data and pattern analysis in speech recognition, Read more...

 
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