Modelling and segmenting subunits for sign language recognition based on hand motion analysis
Pattern Recognition Letters
Shape recognition for Irish sign language understanding
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Multiple people gesture recognition for human-robot interaction
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Real-time vision based gesture recognition for human-robot interaction
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
A fast algorithm for hand gesture recognition using relief
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
A data-mining based skin detection method in JPEG compressed domain
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
A vision-based gesture recognition system for human-robot interaction
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Non-rigid shape recognition for sign language understanding
WSEAS TRANSACTIONS on SYSTEMS
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Skin segmentation is the cornerstone of many applications such as gesture recognition, face detection, and objectionable image filtering. In this paper, we attempt to address the skin segmentation problem for gesture recognition. Initially, given a gesture video sequence, a generic skin model is applied to the first couple of frames to automatically collect the training data. Then, an SVM classifier based on active learning is used to identify the skin pixels. Finally, the results are improved by incorporating region segmentation. The proposed algorithm is fully automatic and adaptive to different signers. We have tested our approach on the ECHO database. Comparing with other existing algorithms, our method could achieve better performance.