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In this paper, the texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network (PNN) for representing the distribution of feature vectors of each texture class in order to generate a feature-label interaction constraint. This distribution of features for each class is assumed as(More)
This paper describes the use of a neural network architecture for classifying textured images in an unsupervised manner using image-specific constraints. The texture features are extracted by using two-dimensional (2-D) Gabor filters arranged as a set of wavelet bases. The classification model comprises feature quantization, partition, and competition(More)
A supervised texture segmentation scheme is proposed in this article. The texture features are extracted by filtering the given image using a filter bank consisting of a number of Gabor filters with different frequencies, resolutions, and orientations. The segmentation model consists of feature formation, partition, and competition processes. In the feature(More)
In this article, we present a two-stage neural network structure that combines the characteristics of self-organizing map (SOM) and multilayer perceptron (MLP) for the problem of texture classification. The texture features are extracted using a multichannel approach. The channels comprise of a set of Gabor filters having different sizes, orientations, and(More)
OBJECTIVE To assess the magnitude and determinants of vitamin A deficiency (VAD) and coverage of vitamin A supplementation (VAS) among pre-school children. DESIGN A community-based cross-sectional study was carried out by adopting a multistage, stratified, random sampling procedure. SETTING Rural areas of eight states in India. SUBJECTS Pre-school(More)
BACKGROUND & OBJECTIVES The present study was carried out on stored rice variety PAU 201 in Punjab that was not permitted for milling and public distribution due to the presence of damaged grains at levels exceeding the regulatory limits of 4.75 per cent. The aim of the study was to determine fungal and aflatoxin contamination in the rice samples to assess(More)
— This paper describes the use of a neural network architecture for classifying textured images in an unsupervised manner using image-specific constraints. The texture features are extracted by using two-dimensional (2-D) Gabor filters arranged as a set of wavelet bases. The classification model comprises feature quantization, partition, and competition(More)
— In this paper, the texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network (PNN) for representing the distribution of feature vectors of each texture class in order to generate a feature-label interaction constraint. This distribution of features for each class is assumed(More)
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