Paper Info
Title
Recognition of local features for camera-based sign language recognition system
Abstract
A sign language recognition system is required to use information from both global features, such as hand movement and location, and local features, such as hand shape and orientation. We present an adequate local feature recognizer for a sign language recognition system. Our basic approach is to represent the hand images extracted from sign-language images as symbols which correspond to clusters by a clustering technique. The clusters are created from a training set of extracted hand images so that a similar appearance can be classified into the same cluster on an eigenspace. The experimental results indicate that our system can recognize a sign language word even in two-handed and hand-to-hand contact cases
Year
DOI
Venue
2000
10.1109/ICPR.2000.903050
ICPR
Keywords
Field
DocType
hand shape,hand location,global features,camera-based sign language recognition system,basic approach,image representation,camera-based sign language recognition,pattern clustering,local features,hand image,sign language word,hand-to-hand contact,karhunen-loeve transforms,hand images,sign language recognition system,adequate local feature recognizer,hand movement,two-handed signing,gesture recognition,eigenvalues and eigenfunctions,local feature,clustering technique,hand orientation,sign language,image recognition,intelligent systems,data mining,computer vision,shape
Training set,Computer vision,Pattern recognition,Intelligent decision support system,Recognition system,Pattern clustering,Computer science,Image representation,Gesture recognition,Sign language,Artificial intelligence,Cluster analysis
Conference
Volume
ISSN
ISBN
4
1051-4651
0-7695-0750-6
Citations 
PageRank 
References 
29
2.37
3
Authors
6
Name
Order
Citations
PageRank
i imagawa1292.37
hirofumi matsuo2292.37
r taniguchi3292.37
daisaku arita4333.28
Shan Lu5293.05
Seiji Igi68314.01