A Framework for Sign Language Recognition | Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Sub-units for Recognition

George Awad

ISBN 10: 3639126580 / ISBN 13: 9783639126587

Published by VDM Verlag Dr. Müller, 2010

About this Item

A Framework for Sign Language Recognition | Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Sub-units for Recognition | George Awad | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2010 | VDM Verlag Dr. Müller | EAN 9783639126587 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Seller Inventory # 101166764

Bibliographic Details

• Title: A Framework for Sign Language Recognition | Applying Support Vector Machines and Active Learning for Skin Segmentation and Boosted Temporal Sub-units for Recognition
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2010
• Language: English
• ISBN 10: 3639126580
• ISBN 13: 9783639126587
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 259 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book describes new techniques that can be used in a sign language recognition (SLR) system, and more generally in human gesture systems. Any SLR system consists of three main components: Skin detector, Tracker, and Recognizer. The skin detector is responsible for segmenting skin objects like the face and hands from video frames. The tracker keeps track of the hand location (specifically the bounding box) and detects any occlusions that might happen between any skin objects. Finally, the recognizer tries to classify the performed sign into one of the sign classes in our vocabulary using the set of features and information provided by the tracker. Instead of dealing with the whole sign for recognition, the sign can be broken down into elementary subunits, which are far less in number than the total number of signs in the vocabulary. We propose a novel algorithm to model and segment these subunits, then try to learn the informative combinations of subunits/features using a boosting framework. In brief, This book takes you into a journey and describes all the necessary steps that are needed to recognize the meaning of a performed signs in a video.