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We study the codeword distribution for a conscience-type competitive learning algorithm, frequency sensitive competitive learning (FSCL), using one-dimensional input data. We prove that the asymptotic codeword density in the limit of large number of codewords is given by a power law of the form Q(x)=C.P(x)(alpha), where P(x) is the input data density and(More)
Vector quantization (VQ) has long been a popular technique for data compression due in part to results from rate-distortion theory that show that VQ is asymptotically optimal for the coding of a stationary source. This optimality has inspired a number of algorithms, collectively known as adaptive vector quantization (AVQ), which apply heuristically(More)
—This paper presents an object tracking method for object-based video processing which uses a two-dimensional (2-D) Gabor wavelet transform (GWT) and a 2-D golden section algorithm. An object in the current frame is modeled by local features from a number of the selected feature points, and the global placement of these feature points. The feature points(More)
SUMMARY An efficient method for simulating instruction sets is described. The method allows for compiled instruction set simulation using the macro expansion capabilities found in many languages. Additionally, we show how the semantics of the C case statement allows instruction branching to be incorporated in an efficient manner. The method is compared with(More)
In this paper, we describe a new adaptive vector quantization (AVQ) algorithm designed for the coding of nonstationary sources. This new algorithm, generalized threshold replenishment (GTR), diiers from prior AVQ algorithms in that it features an explicit, online consideration of both rate and distortion. Rate-distortion cost criteria are used in both the(More)