This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV c...

We present a new class of asymptotically good, linear error-correcting codes based upon expander graphs. These codes have linear time sequential decoding algorithms , logarithmic time parallel decoding algorithms with a linear number of processors, and are simple to understand. We present both randomized and explicit constructions for some of these codes. Experimental results demonstrate the ex...

This report presents a formalisation of the IEEE standard for binary floating-point arithmetic in the set-theoretic specification language Z. The formal specification is refined into four sequential components which unpack the operands, perform the arithmetic, pack and round the result. This refinement follows proven rules and so demonstrates a mathematically rigorous method of program developm...

A novel digitally programmable floating impedance converter circuit is realized using two CMOS digitally programmable differential voltage current conveyors and three grounded passive elements. The realized impedance converter can provide digitally programmable floating impedances like ideal floating resistor, capacitor, inductor and frequency dependent negative resistor through appropriate sel...

The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to find a single model that adequately characterizes the data in a training sample. However, during model selection a sequence of models is generated that consists of the best-fitting model at each level of model complexity. The ...

The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to nd a single model that adequately characterizes the data in a training sample. However, during model selection a sequence of models is generated that consists of the best{{tting model at each level of model complexity. The Nai...

Selecting an optimal subset from original large feature set in the design of pattern classi"er is an important and di$cult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and boundmethod, etc., and most other suboptimal methods proposed recently, such as genetic algorithm and sequential...

We propose a fully automated algorithm that is able to select a discriminative feature set from a training database via sequential forward selection (SFS), sequential backward selection (SBS), and F-score methods. We applied this scheme to microcalcifications cluster (MCC) detection in digital mammograms for early breast cancer detection. The system was able to select features fully automatical...

We propose a sequential forward feature selection method to find a subset of features that are most relevant to the classification task. Our approach uses novel estimation of the conditional mutual information between candidate feature and classes, given a subset of already selected features which is utilized as a classifier independent criterion for evaluation of feature subsets. The proposed ...