Overview
- Gives readers a comprehensive analysis on gesture recognition, defining a new taxonomy for the field
- Focusses on supervised machine learning methods for gesture recognition
- Presents an open-source C++ library for real-time gesture recognition
- Reviews recent research involving deep learning architectures in order to deal with gesture and action recognition problems
- Includes supplementary material: sn.pub/extras
- Includes supplementary material: sn.pub/extras
Part of the book series: The Springer Series on Challenges in Machine Learning (SSCML)
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Table of contents (19 chapters)
Keywords
About this book
This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.
Editors and Affiliations
Bibliographic Information
Book Title: Gesture Recognition
Editors: Sergio Escalera, Isabelle Guyon, Vassilis Athitsos
Series Title: The Springer Series on Challenges in Machine Learning
DOI: https://doi.org/10.1007/978-3-319-57021-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-57020-4Published: 31 July 2017
Softcover ISBN: 978-3-319-86059-6Published: 02 August 2018
eBook ISBN: 978-3-319-57021-1Published: 19 July 2017
Series ISSN: 2520-131X
Series E-ISSN: 2520-1328
Edition Number: 1
Number of Pages: XII, 578
Number of Illustrations: 44 b/w illustrations, 170 illustrations in colour
Topics: Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition