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Gesture recognition

Athitsos, Vassilis.

Gesture recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Gesture recognitionedited by Sergio Escalera, Isabelle Guyon, Vassilis Athitsos.
other author:	Escalera, Sergio.
Published:	Cham :Springer International Publishing :2017.
Description:	xii, 578 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	GestureData processing.
Online resource:	http://dx.doi.org/10.1007/978-3-319-57021-1
ISBN:	9783319570211$q(electronic bk.)

Gesture recognition
Gesture recognition[electronic resource] /edited by Sergio Escalera, Isabelle Guyon, Vassilis Athitsos. - Cham :Springer International Publishing :2017. - xii, 578 p. :ill., digital ;24 cm. - The Springer series on challenges in machine learning,2520-131X. - Springer series on challenges in machine learning..

Preface -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5.

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.

ISBN: 9783319570211$q(electronic bk.)

Standard No.: 10.1007/978-3-319-57021-1doiSubjects--Topical Terms:

789234
Gesture
--Data processing.

LC Class. No.: TK7882.P3 / G47 2017

Dewey Class. No.: 006.42

Gesture recognition
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245 0 0 $a Gesture recognition $h [electronic resource] / $c edited by Sergio Escalera, Isabelle Guyon, Vassilis Athitsos.
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505 0 $a Preface -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5.
520 $a 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.
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