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Deep Structure, Singularities, and Computer Vision

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Cover of 'Deep Structure, Singularities, and Computer Vision'

Table of Contents

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    Book Overview
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    Chapter 1 Blurred Correlation Versus Correlation Blur
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    Chapter 2 A Scale Invariant Covariance Structure on Jet Space
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    Chapter 3 Essential Loops and Their Relevance for Skeletons and Symmetry Sets
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    Chapter 4 Pre-symmetry Sets of 3D Shapes
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    Chapter 5 Deep Structure of Images in Populations Via Geometric Models in Populations
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    Chapter 6 Estimating the Statistics of Multi-object Anatomic Geometry Using Inter-object Relationships
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    Chapter 7 Histogram Statistics of Local Model-Relative Image Regions
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    Chapter 8 The Bessel Scale-Space
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    Chapter 9 Linear Image Reconstruction from a Sparse Set of α -Scale Space Features by Means of Inner Products of Sobolev Type
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    Chapter 10 A Riemannian Framework for the Processing of Tensor-Valued Images
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    Chapter 11 From Stochastic Completion Fields to Tensor Voting
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    Chapter 12 Deep Structure from a Geometric Point of View
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    Chapter 13 Maximum Likely Scale Estimation
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    Chapter 14 Adaptive Trees and Pose Identification from External Contours of Polyhedra
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    Chapter 15 Exploiting Deep Structure
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    Chapter 16 Scale-Space Hierarchy of Singularities
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    Chapter 17 Computing 3D Symmetry Sets; A Case Study
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    Chapter 18 Irradiation Orientation from Obliquely Viewed Texture
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    Chapter 19 Using Top-Points as Interest Points for Image Matching
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    Chapter 20 Transitions of Multi-scale Singularity Trees
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    Chapter 21 A Comparison of the Deep Structure of α -Scale Spaces
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    Chapter 22 A Note on Local Morse Theory in Scale Space and Gaussian Deformations
Attention for Chapter 11: From Stochastic Completion Fields to Tensor Voting
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Chapter title
From Stochastic Completion Fields to Tensor Voting
Chapter number 11
Book title
Deep Structure, Singularities, and Computer Vision
Published by
Springer, Berlin, Heidelberg, January 2005
DOI 10.1007/11577812_11
Book ISBNs
978-3-54-029836-6, 978-3-54-032097-5
Authors

Markus van Almsick, Remco Duits, Erik Franken, Bart ter Haar Romeny, van Almsick, Markus, Duits, Remco, Franken, Erik, ter Haar Romeny, Bart

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 42%
Professor 2 17%
Student > Doctoral Student 1 8%
Student > Master 1 8%
Researcher 1 8%
Other 1 8%
Unknown 1 8%
Readers by discipline Count As %
Computer Science 3 25%
Engineering 2 17%
Medicine and Dentistry 2 17%
Immunology and Microbiology 1 8%
Earth and Planetary Sciences 1 8%
Other 0 0%
Unknown 3 25%