ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Tracking and recognising hand gestures using statistical shape models
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A System for Person-Independent Hand Posture Recognition against Complex Backgrounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hand sign recognition from intensity image sequences with complex backgrounds
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Segmenting Hands of Arbitrary Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Learning-based hand sign recognition using SHOSLIF-M
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Tracking Articulated Hand Motion with Eigen Dynamics Analysis
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Hand shape estimation under complex backgrounds for sign language recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Skin Color Profile Capture for Scale and Rotation Invariant Hand Gesture Recognition
Gesture-Based Human-Computer Interaction and Simulation
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This paper presents a method to segment the hand over complex backgrounds, such as the face. The similar colors and texture of the hand and face make the problem particularly challenging. Our method is based on the concept of an image force field. In this representation each individual image location consists of a vector value which is a nonlinear combination of the remaining pixels in the image. We introduce and develop a novel physics based feature that is able to measure regional structure in the image thus avoiding the problem of local pixel based analysis, which break down under our conditions. The regional image structure changes in the occluded region during occlusion. Elsewhere the regional structure remains relatively constant. We model the regional image structure at all image locations over time using a Mixture of Gaussians (MoG) to detect the occluded region in the image. We have tested the method on a number of sequences demonstrating the versatility of the proposed approach.