This paper study about variants object detection by using localbinarypattern. local binary pattern is one of the famous method in object detection field because of its success used in object detection. The objective...
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ISBN:
(纸本)9781467387804
This paper study about variants object detection by using localbinarypattern. local binary pattern is one of the famous method in object detection field because of its success used in object detection. The objective of object detection is to differentiate between object and background. However, LBP also has its own weaknesses in object detection. LBP modification that been proposed by a lot of researchers can overcome the weaknesses. In this paper, variants of local binary pattern method and modification has been study and analyze. All those local binary pattern modification has been extract its feature in term of object detection. The modifications are Non-Redundant local binary pattern (NRLBP), Integral local binary pattern (INTLBP), Multi-scale Block local binary pattern (MBLBP) and Discriminative Robust local binary pattern (DRLBP).
As face is a topological object, spatial contents contained in facial images (i.e. eyes, nose...) play an important role in feature extraction. To preserve spatial information, region decomposition is an essential ste...
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ISBN:
(纸本)9781424418206
As face is a topological object, spatial contents contained in facial images (i.e. eyes, nose...) play an important role in feature extraction. To preserve spatial information, region decomposition is an essential step in face recognition for local feature based methods. In this paper, a new region decomposition method is proposed based on Cellular Neural Network (CNN). This method, called Face Penta-Chotomy (FPC), can be factorized into two parts. First, a stable facial region is extracted by a CNN template. Then other four regions are depicted according to the stable facial region and facial proportion. The local binary pattern (LBP) is adopted as the region descriptor. This method is evaluated by conducting experiments on the Vale face database B and ORL database. Besides, it compared with six state-of-the-art methods. From experimental results, it outperforms all the compared methods and the feature dimension can be significantly reduced compared with the conventional uniform region decomposition method. Moreover, the proposed method is demonstrated to be robust under single training condition.
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 Texture Feature Extraction (TFE) plays an important role in satellite image processing application. This paper proposes a novel method for Satellite Imagery Classification. Our proposed method is a combination of ...
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ISBN:
(纸本)9781479976133
The Texture Feature Extraction (TFE) plays an important role in satellite image processing application. This paper proposes a novel method for Satellite Imagery Classification. Our proposed method is a combination of local binary pattern (LBP) and Fuzzy c-means classification algorithm. local binary pattern is calculated by thresholding a 3 x 3 neighborhood of each pixel by the center pixel value. During the Feature Extraction Phase, local binary pattern extracts the important characteristics from the satellite images. Fuzzy c-means algorithm classifying the images into different classes. This is a very challenging task in texture feature extraction being used in satellite images.
In this paper, an active shape model (ASM) based facial feature localization strategy is proposed, which employs a local binary pattern (LBP) probability model. Due to the computation simplicity and illumination insen...
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ISBN:
(纸本)9781424479948
In this paper, an active shape model (ASM) based facial feature localization strategy is proposed, which employs a local binary pattern (LBP) probability model. Due to the computation simplicity and illumination insensitivity of LBP texture descriptor and the learning ability of the probability model, the algorithm is robust and fast. In addition, component-based ASM is used to impose reasonable constraints on the shape. Multi-state shape and texture models with state classifier are trained to handle highly flexible components, i.e. eyes and mouth. Our database consisting of tens of persons with various expressions and illuminations is used to train and verify the proposed algorithm. The experiments demonstrate its accuracy, efficiency and robustness.
In this paper we present an approach for face recognition under varying poses, illumination variation and facial expressions. Illumination variation, Pose variation and facial expressions are the main challenges among...
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ISBN:
(纸本)9781479984367
In this paper we present an approach for face recognition under varying poses, illumination variation and facial expressions. Illumination variation, Pose variation and facial expressions are the main challenges among the various factors that severely affects the performance of the face recognition. The main aim of this paper is to calculate and evaluate the performance of combination of Independent Component Analysis and local binary pattern approach for different face databases that contains number of images with illumination variation, varying poses and facial expressions. 3 images per subject are used for training purpose and remaining images for testing or recognition purpose. Uniform local binary pattern is used for extracting the features. These extracted features reduced by independent component analysis and Euclidean distance is used for matching. We have calculated FRR and TSR parameter which gives accuracy of given method. Finally comparing the results for different face databases.
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named local binary pattern Network (LBPNet), is proposed to ...
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ISBN:
(纸本)9781467399616
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named local binary pattern Network (LBPNet), is proposed to efficiently extract and compare high-level over-complete features in multilayer hierarchy. The LBPNet retains the same topology of Convolutional Neural Network (CNN) - one of the most well studied deep learning architectures - whereas the trainable kernels are replaced by the off-the-shelf computer vision descriptor (i.e., LBP). This enables the LBPNet to achieve a high recognition accuracy without requiring any costly model learning approach on massive data. Through extensive numerical experiments using the public benchmarks (i.e., FERET and LFW), LBPNet has shown that it is comparable to other unsupervised methods.
Acute Lymphoblastic Leukemia (ALL) is caused due to increase in number of abnormal lymphocyte cells in blood or bone marrow. This paper presents a methodology for automatic detection of the abnormal lymphocytes in a g...
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ISBN:
(纸本)9781479923618
Acute Lymphoblastic Leukemia (ALL) is caused due to increase in number of abnormal lymphocyte cells in blood or bone marrow. This paper presents a methodology for automatic detection of the abnormal lymphocytes in a given image of the blood sample. We have used local binary pattern (LBP) features for classifying the lymphocyte cell as blast or normal. LBP texture features of blood nucleus are investigated for the detection of ALL. We have also used shape features for classification and a comparative analysis of both the features is performed. It is seen that the LBP features provide reasonably good accuracy in classification.
local binary pattern is a descriptor whose purpose is to summarize the local structure of the images. The goal is to be able to discriminate different images. This method has gone through a large number of changes and...
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