Paper Info
Title
Manipulative Hand Gesture Recognition Using Task Knowledge for Human Computer Interaction
Abstract
This paper presents a system recognizing manipulative hand gestures like grasping, moving, holding can object with both hands, and extending or shortening of the abject in the virtual world using task knowledge. We use two kinds oft ask Lnowledge. One is represented by a state transition diagram, each state of which indicates possible gestures at the next moment. Image features obtained from extracted hand regions are used to judge state transition. When we use a gesture recognition system, we sometimes move our hands unintentionally. To solve this problem, our system has a rest state in the state transition diagram. All unintentional actions are considered as taking a rest and ignored. In addition, the system can recognize collaborative gestures with both hands. They are expressed in a single state so that the complexity in combination of gestures of each hand can be avoided. The second type of knowledge is the situational knowledge to help a user to relieve his/her burden of specifying details about the selection of a target object and the positional relationships of the objects. The vision system can give only limited spatial resolution. Thus, indicating exact position by hand gestures alone is sometimes difficult. This knowledge assists the user in such cases. We have realized an experimental human interface system. Operational experiments show promising results.
Year
DOI
Venue
1998
10.1109/AFGR.1998.670992
FG
Keywords
Field
DocType
task knowledge,manipulative hand gesture recognition,human computer interaction,gesture recognition,state transition diagram,spatial resolution,human interface,resting state,image recognition,vision system,state transition,image features,user interfaces,virtual worlds
Computer vision,Machine vision,Computer science,Feature (computer vision),Gesture,State diagram,Gesture recognition,Human–computer interaction,Artificial intelligence,Situational ethics,User interface,Human interface device
Conference
ISBN
Citations 
PageRank 
0-8186-8344-9
14
2.23
References 
Authors
0
3
Name
Order
Citations
PageRank
Kang-Hyun Jo1884123.08
Kuno, Y.2274.86
Yoshiaki Shirai31300631.73