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Computer Vision, Graphics, and Image Processing
Toward a vision-based hand gesture interface
VRST '94 Proceedings of the conference on Virtual reality software and technology
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
User-independent online gesture recognition by relative motion extraction
Pattern Recognition Letters
Application of Baddeley's distance to dissimilarity measurement between gray scale images
Pattern Recognition Letters
Unencumbered Gestural Interaction
IEEE MultiMedia
A Theoretical Consideration of Pattern Space Trajectory for Gesture Spotting Recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Toward the use of gesture in traditional user interfaces
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Skin Color Region Detection in MPEG Video Sequences
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
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ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Vision-based hand interaction and its application in pervasive games
Proceedings of the 8th International Conference on Virtual Reality Continuum and its Applications in Industry
Hand posture recognition using real-time artificial evolution
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Markerless human-robot interface for dual robot manipulators using Kinect sensor
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Static hand gesture recognition using neural networks
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This paper discusses the use of the computer vision in the interpretation of human gestures. Hand gestures would be an intuitive and ideal way of exchanging information with other people in a virtual space, guiding some robots to perform certain tasks in a hostile environment, or interacting with computers. Hand gestures can be divided into two main categories: static gestures and dynamic gestures. In this paper, a novel dynamic hand gesture recognition technique is proposed. It is based on the 2D skeleton representation of the hand. For each gesture, the hand skeletons of each posture are superposed providing a single image which is the dynamic signature of the gesture. The recognition is performed by comparing this signature with the ones from a gesture alphabet, using Baddeley's distance as a measure of dissimilarities between model parameters.