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Machine Learning for Vision-Based Motion Analysis

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Cover of 'Machine Learning for Vision-Based Motion Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis
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    Chapter 2 Riemannian Manifold Clustering and Dimensionality Reduction for Vision-Based Analysis
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    Chapter 3 Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models
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    Chapter 4 Mixed-State Markov Models in Image Motion Analysis
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    Chapter 5 Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate
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    Chapter 6 Discriminative Multiple Target Tracking
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    Chapter 7 A Framework of Wire Tracking in Image Guided Interventions
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    Chapter 8 An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos
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    Chapter 9 Video-Based Human Motion Estimation by Part-Whole Gait Manifold Learning
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    Chapter 10 Spatio-Temporal Motion Pattern Models of Extremely Crowded Scenes
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    Chapter 11 Learning Behavioral Patterns of Time Series for Video-Surveillance
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    Chapter 12 Recognition of Spatiotemporal Gestures in Sign Language Using Gesture Threshold HMMs
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    Chapter 13 Learning Transferable Distance Functions for Human Action Recognition
Attention for Chapter 7: A Framework of Wire Tracking in Image Guided Interventions
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Chapter title
A Framework of Wire Tracking in Image Guided Interventions
Chapter number 7
Book title
Machine Learning for Vision-Based Motion Analysis
Published by
Springer, London, January 2011
DOI 10.1007/978-0-85729-057-1_7
Book ISBNs
978-0-85729-056-4, 978-0-85729-057-1
Authors

Peng Wang, Andreas Meyer, Terrence Chen, Shaohua K. Zhou, Dorin Comaniciu

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Student > Master 2 40%
Researcher 1 20%
Readers by discipline Count As %
Engineering 3 60%
Medicine and Dentistry 1 20%
Computer Science 1 20%

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