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Automatic gender detection through facial features has become a critical component in the new domain of computer human observation and computer human interaction (HCI). Automatic gender detection has numerous applications in the area of recommender systems, focused advertising, security and surveillance. Detection of gender by using the facial features is done by many methods such as Gabor wavelets,...
LPR (License Plate Recognition) is a foundation component of modern transportation management systems. It uses a set of computer image-processing technologies to identify vehicle by its license plate. Character recognition is the core of LPR, which is essentially a multi-classification problem. The challenge is how to recognize every character of the license plate accurately and rapidly in case of...
Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational decision support systems for the early detection of glaucoma can help prevent this complication. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser...
The objective of this paper is to evaluate the classification performance of several feature extraction and classification methods for exotic wood texture images as dataset. The Gray Level Co-occurrence Matrix, Local Binary Patterns, Wavelet, Ranklet, Granulometry, and Laws' Masks will be used to extract features from the images. The extracted features are then fed into five classification techniques:...
In many researches, valuable studies have been done for feature extraction from images data-base, but because of weak classifiers using, good results have not been achieved. In this paper, different classifiers are compared in order to increase image retrieval system precision. Five different classifiers are used in the paper: the support vector-machine, the MLP neural network, the K-nearest neighbor,...
Method of image recognition based on statistics can achieve fine performance only if large numbers of samples are provided. In some situation, it's impossible to obtain so many samples, which may result in the poor recognition-performance because lacking of information. Furthermore, frequently-used neural network is designed as classifier with the purpose of empirical risk minimization and with poor...
Support Vector Machine (SVM) is a very popular arithmetic, based on SVM, developed a paper defects recognition system. In the stage of paper defects image segmentation, proposed a algorithm based on the SVM, While in the stage of paper defects feature extraction, applied a multi-class SVM to classify the paper defects. Experimental results show that the proposed system yields faster recognition speed...
To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
Support vector machine (SVM) is discussed to use for recognizing cucumber leaf diseases in this paper. Considering that it is a small number of samples, a new experimental program has been proposed which takes each spot of leaves as a sample instead of taking each leaf as a sample. In the experiments Radial Basis Function (RBF), polynomial and Sigmoid kernel function were also used to carry out comparative...
The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are applied to provide feature extraction from the...
In order to resolve the problem incurred by low efficient manual classification of tremendous aurora images, an automatic aurora images classification system for huge dataset application is proposed. First, static aurora images are decomposed into texture part and cartoon part with a method called Morphological Component Analysis (MCA). Then features extracted from texture part are classified by three...
India is a multi-lingual multi-script country, where eighteen official scripts are accepted and there are over hundred regional languages. In this paper we propose a zone-based hybrid feature extraction algorithm scheme towards the recognition of off-line handwritten numerals of two popular south-Indian scripts. The character centroid is computed and the character/numeral image (50 ?? 50) is further...
We propose a new ultrasonic image analysis system that can be utilized as an effective tool in classifying liver states as normal, hepatitis, or liver cirrhosis. In this system, we first define suitable settings for the ultrasonic device, then remove the inhomogeneous structures from the area of interest in the image, and then, by using the forward sequential search method, look for the useful texture...
Processing and classifying galaxy information is one of the most important challenges and intensive research area for astronomers. In this paper; analyzing and classifying photographic images of galaxies are presented, with interesting scientific findings gleaned from the processed photographic data. In addition, the performance of ten artificial neural networks (ANNs) based classifiers was evaluated,...
In this paper, classification of pavement surface distress and the statistics of the distress data are discussed. In order to improve the accuracy and efficiency to identify the pavement surface distress by the image information, a new algorithm based on SVM is discussed. In this study, support vector classification (SVC), which is a novel and effective classification algorithm, is applied to crack...
Within the context of a traffic scenario, pedestrians may have several attitudes or perform different actions: wait at the traffic light, cross the street, run for a bus or a taxi, walk or run on the pavement. When performing all these actions, pedestrians have different attitudes: stand, walk, run. We have studied those attitudes and the contexts in which they appear and we have derived some semantic...
Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with Backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition...
We develop a new approach for gender recognition. In this paper, our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present a gender identifier. We then use nonlinear...
Satellite images have been extensively used for rainfall estimation predictive models based on pattern recognition techniques, even so unsupervised and supervised. However, most of these kind of data are unlabeled, and the acquisition of labeled data for a learning problem often requires a skilled human agent to manually classify training examples. In this paper we introduce the use of semi-supervised...
Text and non-text segmentation and classification is very important in document layout analysis system before it is presented to an OCR system. Heuristic rules have been used in segmenting and classifying the text and non-text blocks. This research focuses on the classification of non-text block in technical documents into table, graph, and figure. A comparative study is conducted between backpropagation...
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