ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
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
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Recognition of Shape-Changing Hand Gestures Based on Switching Linear Model
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Tracking Articulated Hand Motion with Eigen Dynamics Analysis
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
MRF Augmented Particle Filter Tracker
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Real-time Hand Tracking With Variable-Length Markov Models of Behaviour
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
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
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Towards Communicative Face Occlusions: Machine Detection of Hand-over-Face Gestures
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Journal of Network and Computer Applications
Features extraction from hand images based on new detection operators
Pattern Recognition
Head tracking and hand segmentation during hand over face occlusion in sign language
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Hand detection and feature extraction for static Thai Sign Language recognition
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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The ability to segment or track the hand is an important problem in computer vision. While various solutions have been proposed, many methods do not work against complex or cluttered backgrounds. Solving these cases is essential to solving many problems in the domain of computer vision such as, human-computer interaction (HCI), surveillance, and virtual reality (i.e., augmented desks). 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. The method is not restricted to only segmenting hands across faces and uses no knowledge of hands. Our method is based on the underlying concept of an image force field. In this representation change is measured through how particles move through the field. 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 breaks down under our conditions. The regional image structure changes in the occluded region during occlusion, while 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.