Motion-based recognition
著者
書誌事項
Motion-based recognition
（Computational imaging and vision, v. 9）
Kluwer Academic Publishers, c1997
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内容説明・目次
内容説明
Motion-based recognition deals with the recognition of an object and/or its motion, based on motion in a series of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consist of a complex and coordinated series of events. Unlike much previous research in motion, this approach does not require explicit reconstruction of shape from the images prior to recognition.
This book provides the state-of-the-art in this rapidly developing discipline. It consists of a collection of invited chapters by leading researchers in the world covering various aspects of motion-based recognition including lipreading, gesture recognition, facial expression recognition, gait analysis, cyclic motion detection, and activity recognition.
Audience: This volume will be of interest to researchers and post- graduate students whose work involves computer vision, robotics and image processing.
目次
Preface. 1. Visual Recognition of Activities, Gestures, Facial Expressions and Speech: An Introduction and a Perspective. I: Human Activity Recognition. 2. Estimating Image Motion Using Temporal Multi-Scale Models of Flow and Acceleration. 3. Learning Deformable Models for Tracking the Human Body. 4. Cyclic Motion Analysis Using the Period Trace. 5. Temporal Texture and Activity Recognition. 6. Action Recognition Using Temporal Templates. 7. Human Activity Recognition. 8. Human Movement Analysis Based on Explicit Motion Models. II: Gesture Recognition and Facial Expression Recognition. 9. State-Based Recognition of Gesture. 10. Real- Time American Sign Language Recognition from Video Using Hidden Markov Models. 11. Recognizing Human Motion Using Models of Optical Flow. 12. Facial Expression Recognition Using Image Motion. III: Lipreading. 13. Learning Visual Models for Lip Reading. 14. Continuous Automatic Speech Recognition by Lipreading. 15. Visually Recognizing Speech Using Eigensequences.
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