Tracking and recognizing human activities from video is a very challenging task in the field of Computer Vision. In this paper, we aim to recognize human activities by coping with the existing challenges. At first, th...
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Face recognition has been gaining popularity by computer vision researchers over last two decades. Face recognition concerns to identify person from an image set. In general there are three face recognition classes, i...
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During the past few years, digital image forgery detection system has been received a significant attention in the field of analyzing and understanding digital images. A copy-move forgery is introduced in images by co...
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In many texture recognition problems, local binary pattern (LBP) is used as texture descriptor and has achieved outstanding performances. Because of the success on the texture analysis, it is widely studied by many re...
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Breast cancer is the most identified reason for death among women worldwide. New developments in the field of biomedical image processing have enabled the early and effective diagnosis of breast cancer. Therefore, thi...
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The long term diabetes leads to the retinal vascular disease called diabetic retinopathy (DR). As DR is a progressive disease, it should be diagnosed and treated as soon as possible to prevent the patient from blindne...
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Key frames are useful in a wide variety of applications like summarizing, storing, indexing and retrieving video clips etc. In this paper, we present an efficient key frame extraction technique. The present method det...
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This paper presents an efficient and effective way on computing the local binary pattern (LBP) feature from the halftone image for the image retrieval and classification tasks. The Ordered Dither Block Truncation Codi...
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ISBN:
(纸本)9781538627785
This paper presents an efficient and effective way on computing the local binary pattern (LBP) feature from the halftone image for the image retrieval and classification tasks. The Ordered Dither Block Truncation Coding (ODBTC) compresses an image into two new representations, i.e. color quantizer and halftone image. Two image features can be generated from these two new representations for computing similarity degree between several images in the image retrieval and classification processes. Color Histogram Feature (CHF) can be easily computed from color quantizer, whereas the Block-based local binary pattern (BLBP) can be directly applied on halftone image. The feature extraction process avoids the ODBTC decoding step making it very useful in real time application requiring fast feature computation. As documented in the experimental result, the proposed method offers a promising result on the image classification and retrieval tasks compared to that of the former schemes.
In this research work, local binary pattern (LBP)-based automatic target recognition system is proposed for classification of various categories of moving civilian targets using their infrared image signatures. Target...
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ISBN:
(纸本)9789811021046;9789811021039
In this research work, local binary pattern (LBP)-based automatic target recognition system is proposed for classification of various categories of moving civilian targets using their infrared image signatures. Target recognition in infrared images is demanding owing to large variations in target signature and limited target to background contrast. This demands robust features/descriptors which can represent possible variations of the target category with minimal intra class variance. LBP, a simple yet efficient texture operator initially proposed for texture recognition of late is gaining popularity in face and object recognition applications. In this work, the suitability of LBP and two of its variants, local ternary pattern (LTP), complete local binary pattern (CLBP) for the task of recognition in infrared images has been evaluated. The performance of the method is validated with target clips obtained from 'CSIR-CSIO moving object thermal infrared imagery dataset'. The number of classes is four-three different target classes (Ambassador, Auto and Pedestrian) and one class representing the background. Classification accuracies of 89.48 %, 100 % and 100 % were obtained for LBP, LTP and CLBP, respectively. The results indicate the suitability of LBP operator for target recognition in infrared images.
The local binary pattern (LBP) is a simple yet efficient texture operator, and the completed local binary pattern (CLBP) is a completed modeling for LBP that has been adopted in many texture classification methods. Ho...
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The local binary pattern (LBP) is a simple yet efficient texture operator, and the completed local binary pattern (CLBP) is a completed modeling for LBP that has been adopted in many texture classification methods. However, existing CLBP operators are sensitive to noise and they cannot extract the regional structure information efficiently. To overcome these disadvantages, we propose a circular regional mean completed local binary pattern (CRMCLBP) by introducing a circular regional mean operator to modify the traditional CLBP. We also present two encoding schemes for CRMCLBP. The proposed CRMCLBP not only achieves rotation invariance and completed representation capability but also has high robustness to image noise. In order to evaluate the performance, we compare the CRMCLBP with recent state-of-the-art methods by extensive experiments on two popular texture databases including Outex database and Columbia-Utrecht reflection and texture database. Excellent experimental results demonstrate that the proposed CRMCLBP is comparable with recent state-of-the-art texture descriptors and superior to other approaches for robustness. (C) 2018 SPIE and IS&T
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