Efficient deformable template detection and localization without user initialization
Computer Vision and Image Understanding
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting Hands of Arbitrary Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Automatic 2D Hand Tracking in Video Sequences
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A boosted classifier tree for hand shape detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Detecting instances of shape classes that exhibit variable structure
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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For gesture and sign language recognition, hand shape and hand motion are the primary sources of information that differentiate one sign from another. So, building an efficient and reliable hand detector is an important step for recognizing signs and gesture. In this paper we evaluate four features for hand detection: color, temporal motion, gradient norm, and motion residue, and we explore the potential of these features for building a reliable hand detector. At first, we use these four features separately to identify where the hands are in each frame of our gesture videos. Then we evaluate different combinations of such features using weighted linear combination, so to build a more accurate hand detector. Experimental results show the relative performance of the four features in isolation and in different combinations, and demonstrate promising results for detectors that combine these features.