• Part I: Manifold Learning and Clustering/Segmentation.- Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis.- Riemannian Manifold Clustering and Dimensionality Reduction for Vision-based Analysis.- Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models.- Part II: Tracking.- Mixed-state Markov Models in Image Motion Analysis.- Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate.- Discriminative Multiple Target Tracking.- A Framework of Wire Tracking in Image Guided Interventions.- Part III: Motion Analysis and Behavior Modeling.- An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos.- Video-based Human Motion Estimation by Part-whole Gait Manifold Learning.- Spatio-temporal Motion Pattern Models of Extremely Crowded Scenes.- Learning Behavioral Patterns of Time Series for Video-surveillance.- Part IV: Gesture and Action Recognition.- Recognition of Spatiotemporal Gestures in Sign Language using Gesture Threshold HMMs.- Learning Transferable Distance Functions for Human Action Recognition.

From the reviews: "The successes of the First and Second International Workshops on Machine Learning for Vision-Based Motion Analysis, which were held in 2008 and 2009, prompted this book. The book consists of four parts, and each part includes a number of freestanding chapters. ... This book provides a comprehensive introduction to machine learning for vision-based motion analysis. I would recommend it to students and researchers who are interested in learning about the topic." (J. P. E. Hodgson, ACM Computing Reviews, June, 2011)

• Part I: Manifold Learning and Clustering/Segmentation.- Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis.- Riemannian Manifold Clustering and Dimensionality Reduction for Vision-based Analysis.- Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models.- Part II: Tracking.- Mixed-state Markov Models in Image Motion Analysis.- Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate.- Discriminative Multiple Target Tracking.- A Framework of Wire Tracking in Image Guided Interventions.- Part III: Motion Analysis and Behavior Modeling.- An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos.- Video-based Human Motion Estimation by Part-whole Gait Manifold Learning.- Spatio-temporal Motion Pattern Models of Extremely Crowded Scenes.- Learning Behavioral Patterns of Time Series for Video-surveillance.- Part IV: Gesture and Action Recognition.- Recognition of Spatiotemporal Gestures in Sign Language using Gesture Threshold HMMs.- Learning Transferable Distance Functions for Human Action Recognition.

From the reviews: "The successes of the First and Second International Workshops on Machine Learning for Vision-Based Motion Analysis, which were held in 2008 and 2009, prompted this book. The book consists of four parts, and each part includes a number of freestanding chapters. ... This book provides a comprehensive introduction to machine learning for vision-based motion analysis. I would recommend it to students and researchers who are interested in learning about the topic." (J. P. E. Hodgson, ACM Computing Reviews, June, 2011)

• Part I: Manifold Learning and Clustering/Segmentation.- Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis.- Riemannian Manifold Clustering and Dimensionality Reduction for Vision-based Analysis.- Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models.- Part II: Tracking.- Mixed-state Markov Models in Image Motion Analysis.- Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate.- Discriminative Multiple Target Tracking.- A Framework of Wire Tracking in Image Guided Interventions.- Part III: Motion Analysis and Behavior Modeling.- An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos.- Video-based Human Motion Estimation by Part-whole Gait Manifold Learning.- Spatio-temporal Motion Pattern Models of Extremely Crowded Scenes.- Learning Behavioral Patterns of Time Series for Video-surveillance.- Part IV: Gesture and Action Recognition.- Recognition of Spatiotemporal Gestures in Sign Language using Gesture Threshold HMMs.- Learning Transferable Distance Functions for Human Action Recognition.

From the reviews: "The successes of the First and Second International Workshops on Machine Learning for Vision-Based Motion Analysis, which were held in 2008 and 2009, prompted this book. The book consists of four parts, and each part includes a number of freestanding chapters. ... This book provides a comprehensive introduction to machine learning for vision-based motion analysis. I would recommend it to students and researchers who are interested in learning about the topic." (J. P. E. Hodgson, ACM Computing Reviews, June, 2011)