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Local binary patterns (LBP) have emerged as a very powerful discriminatory texture descriptor in biometric trait analysis. Several new extensions of LBP-based texture descriptors have been proposed, focusing on improving robustness to noise by using different encoding or thresholding schemes. In this paper, a new feature set inspired by the completed local binary pattern (CLBP), known as the dynamic...
This paper presents a novel human ear recognition approach based on Multi-scale Local Binary Pattern (MLBP) descriptor to enhance the recognition performance. The proposed method includes the following two steps: (i) the feature extraction step that computes the MLBP descriptor-based features from human ear images, and (ii) the matching process that uses the Kullback Leibler (KL) distance to capture...
This paper presents a model of Pulse-Coupled Neural Network (PCNN) for multispectral image segmentation. Its application for license plate recognition (LPR) is considered; this consists of three processing steps. First step extracts the license plate coordinates from the original image; second step is the PCNN-based segmentation method to obtain a binary image containing only the characters of the...
It describes a method of fault diagnosis fuzzy clustering algorithm and implementation steps, fault diagnosis of glutamic acid fermentation process is to obtain prior knowledge and statistical knowledge, based on fuzzy clustering analysis to find fault symptom cluster centers, discrimination of the new fault signs using fuzzy pattern recognition method, in order to achieve process fault diagnosis...
General questions compressed representation of a given set of binary matrices of the same size by associative masking. Existence and uniqueness are proved: 1) identification in a random binary matrix of one of the matrices of the set of masked patterns of the same size; 2) mistaken recognition on this set of matrices by mask immersed in a random sequence of the inversion of any number of unmasked...
In this study, gender prediction is investigated for the face images. To extract the features of the images, Local Binary Pattern (LBP) is used with its different parameters. To classify the images male or female, K-Nearest Neighbors (KNN) and Discriminant Analysis (DA) methods are used. Their performances according to the LBP parameters are compared. Also classification methods' parameters are changed...
Paddy being the staple food of India is majorly affected by deficiency of primary nutrient elements like nitrogen, phosphorus and potassium. Leaves can be deficient with multiple nutrient elements at a same time. This can alter natural color of paddy leaves. Such leaves are considered as defective. The proposed work is to automate multiple nutrient element deficiency identification of paddy leaves...
This paper presents the development of an Neural Network Based Skeleton Recognition and Sudoku Solving. The main objective of this work is to recognize the number and its corresponding position from a Sudoku image and also to solve any valid Sudoku. The recognition system is designed through an artificial neural network model. The neural network uses the mechanism of feedforwardbackpropogation technique...
The use of pattern recognition and classification has increased in various real world applications such as face recognition and other crucial applications. The key aim, of these applications, is to automate the various complicated task. This paper presents the face recognition application and their investigation. The investigation of the face recognition leads to find some of the most optimum approaches...
As medicine, herbal plants have been widely used since ancient times, and are still used today. There are various types of herbal plants that can be used as medicine but due to the limited ability of communities to recognize the type of plants and the lack of information, both cause the limited use of plants as medicine. In this research, an herbal plants identification system based on leaves pattern...
To identify criminal or pedophile in online child pornography images and video is a challenging task when the faces and other distinguish features are not shown. To address these kind of problems, the system of recognition using androgenic hair pattern is being developed. The system of recognition presented in this paper used three main parts of methods, pre-processing methods, feature extraction...
Support Vector Machines are widely accepted in the field of pattern recognition because of their superiority in performing supervised classification. It is known that all kernel parameters may be used for classification more-or-less precisely (giving rise to vagueness) and also for the same classification problem, there are a number of kernel parameters which give the best accuracy (giving rise to...
Independent component analysis (ICA) is a recent technique used in signal processing for feature description in classification systems, as well as in signal separation, with applications ranging from computer vision to economics. In this paper we propose a preprocessing step in order to make ICA algorithm efficient for rotation invariant feature description of images. Tests were carried out on five...
In this paper, a novel logo recognition algorithm based on a set of invariant features, which are calculated by using Radon transform and complex moments is proposed. This set of features is invariant to Rotation, Scaling, and Translation (RST) and it is also robust to additive noise. Radon transform is powerful tool for rotation, scaling, and translation properties which make it useful for our purpose...
Traditional pattern recognition systems were implemented using neural network systems in order to recognize partial prints ranging from 100%, 75%, 50%, 40% and 30% of the whole fingerprint image. The uniqueness of the patterns of each individual fingerprint served as the motivation in pursuing this research in identifying partial fingerprints where the minutiae pattern identification method cannot...
Partial discharge is a problem that often affects high-voltage equipments. Early diagnosis system for partial discharge can minimize the risk that caused by partial discharge. One of the steps of partial discharge diagnosis is partial discharge signal pattern recognition and judgement system that play a role in determining the type and level of partial discharge, and one of the methods that can be...
In this paper, we propose a new set of separable two-dimensional discrete orthogonal moments called Krawtcouk-Tchebichef's moments. This set of moments is based on the bivariate discrete orthogonal polynomials defined from the product of Krawtchouk and Tchebichef discrete orthogonal polynomials with one variable. We also present a novel set of Krawtchouk-Tchebichef invariant moments. These invariant...
Some pattern recognition techniques may present a high computational cost for learning samples' behaviour. The Optimum-Path Forest (OPF) classifier has been recently developed in order to overcome such drawbacks. Although it can achieve faster training steps when compared to some state-of-art techniques, OPF can be slower for testing in some situations. Therefore, we propose in this paper an implementation...
This paper analyses the influence of quantization of audio signals on the Time Encoding Signal Processing and Recognition S-matrix, in order to detect and classify intruders in wildlife areas. The intruder classification is performed with multilayer feed-forward neural networks. The databases involved in this work consist of 640 waveforms of audio signals originated from 4 different types of sources...
Previous sparse representation (SR) methods are constructed on the assumption that the test sample can be approximately expressed by a linear combination of all original training samples. However, in most real-world applications samples are not subject to this assumption. Consequently, it is significant to explore a new way to improve SR. In this paper, we propose two directional transform based sparse...
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