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Handwriting recognition has always been a challenging task in image processing and pattern recognition. India is a multi-lingual, multi-script country, where eighteen official scripts are accepted and there are over a hundred regional languages. The feature extraction method is probably the most effective method in achieving high recognition performance. In this study we proposed a zone-based feature...
Face Recognition is the process of identification of a person by his facial image. As applied to face recognition, this paper proposes a method, comprising of Laplacian of Gaussian (LoG) filter for intricate facial detail enhancement, Singular Value Decomposition (SVD) for holistic feature extraction and Feed forward Neural Network (FFNN) for classification. Applications of LoG filter highlights,...
We investigate an intelligent computer vision system that incorporates feedforward neural networks (NN) for recognition and classification of commercially available cork tiles. The system is capable of acquiring and processing gray images using several feature generation and analysis techniques. Its functionality includes image acquisition, feature extraction and preprocessing, and feature classification...
This paper, presents an intelligent diagnosis system for electrocardiogram (ECG) intensity images using artificial neural network (ANN). Features are extracted from many preprocess such as wavelet decomposition (WD), Edge detection (ED), gray level histogram (GLH), Fast Fourier transform (FFT), and Mean-variance (M-V). The ANN supervised feed-forward back propagation using adaptive learning rate with...
A novel method is proposed herein for handwritten digit segmentation in historical document images. It is based on one-class classifiers, which are used to distinguish isolated characters from touching characters. In contrast to other techniques based on feed forward neural networks, the proposed method does not require negative data in the training phase. Three methods for feature extraction and...
This paper presents a simple and robust method for recognition of rotated objects by Feedforward Neural Classifier. Initially, the translation invariance is achieved after pre-processing the image. Fourier transform is then applied to each of the rotated binary edge images with 5 degrees interval. Then the 3-level Discrete Wavelet Transform (DWT) is applied to compress the Fourier coefficients. The...
In this paper, a statistical approach based feature selection method for multilayered feedforward neural network for the classification of wood veneer defects is presented. This method focuses on identifying the superfluous input features by defining a Feature Rejection Criteria (FRC). It is based on an analysis of the intra-class and inter-class variation in the features and their correlation within...
An intelligent, automated visual inspection system is investigated in this paper. It is used for pattern recognition and classification of four different types of cork tiles. The process includes image acquisition with a CCD camera, texture feature extraction, statistical processing of the feature vectors, and cork tiles classification with feed-forward Neural Networks (NN) employing a hybrid global...
Palmprint identification is the means of recognizing an individual from the database using his/ her palmprint features. Palmprint is easy to capture, requires cheaper equipment and is more acceptable by the public. Moreover, palmprint is also rich in features. Wavelet transform is a multi-resolution analysis tool that can extract palm lines in different resolution levels. In low-resolution level,...
The authors present a texture image classification system based upon the use of two cascaded multilayer feedforward neural networks (MFNNs). The first network transforms a set of high-dimensional and correlated feature images into another set of uncorrelated principal feature images with its dimensionality being significantly compressed while minimizing the information lost. The second accomplishes...
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