Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
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
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tools and Techniques for Video Performance Evaluation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 13th annual ACM international conference on Multimedia
Dynamic disparity adjustment and histogram-based filtering of range data for fast 3-D hand tracking
Digital Signal Processing
Toward multimodal situated analysis
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
GW'11 Proceedings of the 9th international conference on Gesture and Sign Language in Human-Computer Interaction and Embodied Communication
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We have developed a video hand segmentation tool which can help with generating hands ground truth from sign language image sequences. This tool may greatly facilitate research in the area of sign language recognition. In this tool, we offer a semi automatic scheme to assist with the localization of hand pixels, which is important for the purpose of recognition. A candidate hand generator is applied by using the mean shift image segmentation algorithm and a greedy seeds growing algorithm. After a number of hand candidates is generated, the user can reduce the candidates by simple mouse clicks. The tool also provides a hand tracking function for faster processing and a face detection function for groundtruthing non manual signals. In addition, we provided a two-passes groundtruthing scheme unlike other tools that only does one-pass. Our first pass processing is automatic and does not need user interaction. The experiment results demonstrate that based on the first pass's result, one can groundtruth 10,000+ frames of sign language within 8 hours