An HMM-Based Threshold Model Approach for Gesture Recognition
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
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Feature Selection for Visual Gesture Recognition Using Hidden Markov Models
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Segmentation of the face and hands in sign language video sequences using color and motion cues
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we present a system to recognize the hand motion of Taiwanese Sign Language (TSL) using the Hidden Markov Models (HMMs) through a vision-based interface. Our hand motion recognition system consists of four phases: construction of color model, hand tracking, trajectory representation, and recognition. Our hand tracking can accurately track the hand positions. Since our system is recognized to hand motions that are variant with rotation, translation, symmetric, and scaling in Cartesian coordinate system, we have chosen invariant features which convert our coordinate system from Cartesian coordinate system to Polar coordinate system. There are nine hand motion patterns defined for TSL. Experimental results show that our proposed method successfully chooses invariant features to recognition with accuracy about 90%.