Learn More
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)
|This paper describes hardware that has been built to compress video in real time using full-search vector quan-tization (VQ). This architecture implements a diierential-vector-quantization (DVQ) algorithm and features a special-purpose digital associative memory, the VAMPIRE chip, which has been fabricated in 2m CMOS. We describe the DVQ algorithm, its(More)
We describe hardware that has been built to compress video in real time using full-search vector quantization (VQ). This architecture implements a diierential-vector-quantization (DVQ) algorithm which features entropy-biased codebooks designed using an artiicial neural network (ANN). A special-purpose digital associative memory, the VAMPIRE chip, performs(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)
The focus of this paper is a convergence study of the frequency sensitive competitive learning (FSCL) algorithm. We approximate the final phase of FSCL learning by a diffusion process described by the Fokker-Plank equation. Sufficient and necessary conditions are presented for the convergence of the diffusion process to a local equilibrium. The analysis(More)