One of the long-term goals in human-computer interaction is to utilize more intuitive and natural methods such as speech and hand gestures that a user would employ for communication. In this paper, we present a robust method of hand tracking using a probability map computed from a joint probability function derived from both depth information and skin-tone information. The depth information is provided using a commercially available stereo camera, and the color information is found using calibrated and linearized color information. The work shows the effectiveness of this technique, in terms of both the quality of the results as well as the speed at which the computations may be performed. Due to the linearization of the color information and the use of stereo vision data, the technique is demonstrated to be largely invariant to illumination changes.

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This paper proposed an objective video quality metric designed for automatically assessing the perceived quality of digitally compressed multimedia videos using H.264 video compression. The rationale in proposing perceptual-based metric is because traditional measure, peak signal-to-noise ratio (PSNR), has been found to correlate poorly with subjective quality ratings, particularly at much lower bit rates. In this paper, computational models have been applied to emulate human visual perception based on a combination of local and global modulating factors. The proposed video quality metric has been tested on CIF and QCIF video sequences compressed using H.264 video compression technique at various bit rates (24-384 Kbps) and frame rates (7.5-30Hz). Performance of the proposed metric with respect to subjective scores will be reported and a comparison with PSNR and also the video structural similarity method (being one of the best video quality metric for high bit rate videos recently reported in the literature) will also be provided in this paper.