Non-linear diffusion approaches are an effective way to reduce noise and preserve the edge information. In this paper, the work is extended to integrate the non-linear diffusion and local binary pattern (LBP) textons,...
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ISBN:
(纸本)9781538668665
Non-linear diffusion approaches are an effective way to reduce noise and preserve the edge information. In this paper, the work is extended to integrate the non-linear diffusion and local binary pattern (LBP) textons, where the diffusivity function is adapted to pixels type after LBP classification. This allows smoothing on homogenous and noisy regions but not on edges. The proposed method preserves edges better because the diffusion is controlled taking into account the difference of diagonal neighbors in addition to four nearest neighbors. Experimental results on synthetic and real images illustrate the effective performance of the proposed method.
Face recognition, one of the biometric computer vision research area, is a pattern recognition problem which have been done a dozen times since 1960s and it is still a revolutionary area of research interest for many ...
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ISBN:
(纸本)9781538619742
Face recognition, one of the biometric computer vision research area, is a pattern recognition problem which have been done a dozen times since 1960s and it is still a revolutionary area of research interest for many researchers. Although face recognition is the earliest pattern recognition problem yet its accuracy is not as high as other biometric recognition problems like finger print recognition. Different imaging conditions made it challenging like occlusion of faces by hands or eye-glasses, illumination changes, variation in pose and different facial expressions. In this paper we proposed a robust face recognition technique by using local binary pattern and histogram of oriented gradient feature extractor and descriptors. The work has been conducted by carefully acquired and pre-processed 1300 face images, out of it 1040 images were used for training and the rest 260 images for testing purposes. As LBP operators are not good for extracting edge features of a face image we used HOG to extract edge features and LBP for extracting texture features of a face and finally the extracted features has been trained and classified by using multiclass support vector machines and it has shown good accuracy rate of recognition.
The aim of pattern classification is to put similar patterns into the same cluster. Hence most classifiers employ distance functions or dissimilarity function to measure the dissimilarity of patterns. Some features ar...
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ISBN:
(纸本)9781538670361
The aim of pattern classification is to put similar patterns into the same cluster. Hence most classifiers employ distance functions or dissimilarity function to measure the dissimilarity of patterns. Some features are obtained by measuring the value extracted from the physical features of patterns directly. On the other hand, some features are obtained by counting the number of pattern property observed. Different distance functions will affect the performance of a classifier while different type of features are adopted. Recently, local binary patterns (LBP) descriptor and its variants are the most popular methods for extracting features of detail textures of image patterns. Each LBP feature represents the observed number of a specific code. The Nearest Neighbor (NN) classifier is a simplest and efficient classifier to classify histograms of such pattern features. Here the effects of different distance functions for the NN classifier based on LBP features are studied. The UIUC texture database is chosen for observation. The experimental results reveal that the NN classifier using Hellinger distance function as the dissimilarity function to classify histograms of FbLBP features provides the best performance of classification with 92.4% and 94.1% accuracy under different radii.
Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed a...
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ISBN:
(数字)9781510619425
ISBN:
(纸本)9781510619425
Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of local binary pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.
Excessive dependencies on digital photographs raise the need of their forensic analysis. When some operation is performed to create fake/forged images then it disturbs the image underlying statistics. To reveal these ...
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Excessive dependencies on digital photographs raise the need of their forensic analysis. When some operation is performed to create fake/forged images then it disturbs the image underlying statistics. To reveal these statistics, image features need to be extracted. The local binary pattern (LBP) descriptor provide good results in texture classification, face recognition, image retrieval, and facial expression recognition, and so forth. It is computationally simple and provides crucial information of image internal statistics. In this article we propose a method for image forgery detection based on higher order LBP texture descriptor. In available literature, higher order pixel analysis gives promising results in various applications that motivate us for proposing a method based on higher order analysis. Higher order analysis provides better correlation information between image pixels that is necessary in image forgery detection. This analysis provide correlation information of near and far pixels in balanced manner, that is, near pixels get more weightage in comparison to far pixels. We assess the efficacy of our proposed work on three publicly available databases and it provides better results in comparison to some of the existing techniques.
In this paper, a machine vision system is developed for fluorescent tube defects classification, a new rotation invariant method is presented for texture analysis. The objective of research is to study a new texture a...
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In this paper, we introduced new Age Classification method with Contrast, Correlation, Energy, and local homogeneity features on Central local binary pattern based Structure. local binary pattern is computed on the im...
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Face recognition is the most challenging facets of image processing. Human brain uses the same process to recognize a face. Brain extracts essential features from the face and stores in his database. Next time, brain ...
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Since the appearance of mobile devices, gesture recognition is being a challenging task in the field of computer vision. In this paper, a simple and fast algorithm for static hand gesture recognition for mobile device...
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The Internet of Things (IoT) is gaining more importance in our modern life because of its wide range of applications. In this paper, we propose a novel dog recognition system for recognizing dogs from camera images. T...
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