A novel variable-rate vector quantizer (VQ) design algorithm using both fuzzy and competitive learning technique is presented. The algorithm enjoys better rate-distortion performance than that of other existing fuzzy clustering and competitive learning algorithms. In addition, the learning algorithm is less sensitive to the selection of initial reproduction vectors. Therefore, the algorithm can...

The recognition of ten Thai isolated numerals from zero to nine and 60 Thai polysyllabic words are compared between different recognition techniques, namely, Neural Network, Modified Rackpropagation Neural Network. Fuzzy-Neural Network, and Hidden Markov Model. The I j-state left-to-right discrete hidden markov model in cooperation with the vector quantization technique has been studied and com...

In this paper, we introduce a soft vector quantization scheme with inverse power-function distribution, and analytically derive an upper bound of the resulting quantization noise energy in comparison to that of typical (hard-deciding) vector quantization. We also discuss the positive impact of this kind of soft vector quantization on the performance of machine-learning systems that include one ...

An unconstrained Farsi handwritten word recognition system based on fuzzy vector quantization (FVQ) and hidden Markov model (HMM) for reading city names in postal addresses is presented. Preprocessing techniques including binarization, noise removal, slope correction and baseline estimation are described. Each word image is represented by its contour information. The histogram of chain code slo...

In this paper, fuzzy possibilistic c-means (FPCM) approach based on penalized and compensated constraints are proposed to vector quantization (VQ) in discrete cosine transform (DCT) for image compression. These approaches are named penalized fuzzy possibilistic c-means (PFPCM) and compensated fuzzy possibilistic c-means (CFPCM). The main purpose is to modify the FPCM strategy with penalized or ...

Fuzzy extractors are a powerful tool to extract randomness from noisy data. A fuzzy extractor can extract randomness only if the source data is discrete while in practice source data is continuous. Using quantizers to transform continuous data into discrete data is a commonly used solution. However, as far as we know no study has been made of the effect of the quantization strategy on the perfo...