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The feasibility of automating the evaluation of stroke chronic patients' motor functions has been explored while analyzing their corresponding fMRI studies with statistical parametric analysis, statistical inference analysis and a nonlinear multivoxel pattern-analysis classifier based on a feed-forward backward-propagation neural network. After doing principal component analysis and independent component...
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 a novel method based on fractal features for the classification of mammogram images. For recognition of regions and objects in the natural scenes, there is always a need for features, which are invariant, and they provide a good set of descriptive values for the region. There are numerous methods available to estimate parameters from the images of the fractal surface. In this paper...
Face detection plays an important role in developing human-robot interaction (HRI) for social robots to recognize people. In this paper, we introduce an intelligent vision system that is able to detect human face from background and filter out all the non-face but face-like images. The human face is detected using Ada boost-based Haar-Cascade classifier and the real human face detection is improved...
Hyperspectral data are characterized by a huge size due to hundreds of narrow frequency bands. However, the classes of interest are often characterized by only a few features from the available (ormodified) feature space. Using a few samples of the classes of interest it is possible to identify the features characterizing the classes by calculating the Bhattacharya distance, B or the Jeffries-Matusita...
Urdu compound character recognition is a scarcely developed area and requires robust techniques to develop as Urdu being a family of Arabic script is cursive, right to left in nature and characters change their shapes and sizes when they are placed at initial, middle or at the end of a word. The developed system consists of two main modules segmentation and classification. In the segmentation phase...
The problem of identifying cosmic gamma ray events out of charged cosmic ray background in Cherenkov telescopes is one of the key problems in very high energy gamma ray astronomy. Separation between gamma-like and hadron-like events is performed by a Bayesian ensemble of neural networks and Markov chain Monte Carlo methods for model parameters optimization. The results are discussed in terms of the...
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...
In automatic processing of chromosomes, when chromosomes are segmented from image as objects, some of chromosomes that are close to each other (touching chromosomes) or overlapped, are detected as one object. Objects that have more than one chromosome, must be processed separately from those have one chromosome. One stage of automatic processing of chromosomes is automatic separation of multi-chromosome...
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...
The development of functional magnetic resonance imaging (fMRI) offers promising approaches in the study of human brain function. It dramatically improves an ability to collect large amount of data about brain activity in human subjects performing tasks. Analysis of fMRI is essential for successful detection of cognitive states. This paper presents the use of single hidden-layer feedforward neural...
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 article, we propose a new supervised learning approach for pattern classification applications involving large or imbalanced data sets. In this approach, a clustering technique is employed to reduce the original training set into a smaller set of representative training exemplars, represented by weighted cluster centers and their target outputs. Based on the proposed learning approach, two...
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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