Machine Learning for Vision-based Motion Analysis : Theory and Techniques (Paperback)

Part I: Manifold Learning and Clustering/Segmentation Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis Tomoya Sakai, and Atsushi Imiya Riemannian Manifold Clustering and Dimensionality Reduction for Vision-based Analysis Alvina Goh Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models Fabio Cuzzolin Part II: Tracking Mixed-state Markov Models in Image Motion Analysis Tomas Crivelli, Patrick Bouthemy, Bruno Cernuschi Frias, and Jian-feng Yao Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate Paolo Lombardi, and Cristina Versino Discriminative Multiple Target Tracking Xiaoyu Wang, Gang Hua, and Tony X. Han A Framework of Wire Tracking in Image Guided Interventions Peng Wang, Andreas Meyer, Terrence Chen, Shaohua K. Zhou, and Dorin Comaniciu Part III: Motion Analysis and Behavior Modeling An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos Sunaad Nataraju, Vineeth Balasubramanian, and Sethuraman Panchanathan Video-based Human Motion Estimation by Part-whole Gait Manifold Learning Guoliang Fan, and Xin Zhang Spatio-temporal Motion Pattern Models of Extremely Crowded Scenes Louis Kratz and Ko Nishino Learning Behavioral Patterns of Time Series for Video-surveillance Nicoletta Noceti, Matteo Santoro, and Francesca Odone Part IV: Gesture and Action Recognition Recognition of Spatiotemporal Gestures in Sign Language using Gesture Threshold HMMs Daniel Kelly, John Mc Donald and Charles Markham Learning Transferable Distance Functions for Human Action Recognition Weilong Yang, YangWang, and Greg Mori