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C Charayaphan

Publications and source records attributed to C Charayaphan.

3 recordsLinked to original sources

Optimisation of a computer vision system for the interpretation of American Sign Language.

Presented in this paper is a simulation algorithm for the optimisation of camera position with respect to the signer, to have a full and reliable interpretation of the American Sign Language. The simulation includes a three-dimensional world point into two-dimensional image point transformation algorithm, the effect of the depth information loss and a sign projection correction test. It is concluded that the viewing camera should be positioned at any point in a specified area subtended by a solid angle of 30 degrees, where the centre of the area is located at 45 degrees in the azimuth and 45 degrees in elevation relative to the signer. The theory and the technique are tested with regard to the efficiency of interpreting American Sign Language (ASL) by two adult signers. One of the signers had been using ASL on a regular basis since infancy, and the second signer had signed for the past five years. It is demonstrated that positioning the camera anywhere in the specified area provides a 96 per cent correct interpretation of the 36 signs tested. The results also provide a preliminary indication that signer variability may not present a major problem in interpretation, and that a computer vision system which captures the optimum depth information can distinguish between signs which, to the naked eye, appear to have similar characteristics.

Algorithms↗

Image processing system for interpreting motion in American Sign Language.

In this paper, an image processing algorithm is presented for the interpretation of the American Sign Language (ASL), which is one of the sign languages used by the majority of the deaf community. The process involves detection of hand motion, tracking the hand location based on the motion and classification of signs using adaptive clustering of stop positions, simple shape of the trajectory, and matching of the hand shape at the stop position.

Algorithms↗

Correlation algorithm and sampling techniques for estimating the signal-to-noise ratio of the electrocardiogram.

An algorithm, based on correlation techniques, is proposed for estimating the signal-to-noise ratio of very low frequency signals contaminated by white and flicker noise. Sampling techniques based on converting a single continuous signal into two time series that satisfy the requirements of cross-correlation functions are proposed. The algorithm has been tested on simulated data and the electrocardiogram transduced from ten patients.

Algorithms↗