local binary pattern (LBP) is a simple yet robust texture descriptor that has been widely used in many computer vision applications including face recognition. In this paper, we exploit LBP for handwritten Bangla nume...
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ISBN:
(纸本)9781467366762
local binary pattern (LBP) is a simple yet robust texture descriptor that has been widely used in many computer vision applications including face recognition. In this paper, we exploit LBP for handwritten Bangla numeral recognition. We classify Bangla digits from their LBP histograms using K Nearest Neighbors (KNN) classifier. The performance of three different variations of LBP - the basic LBP, the uniform LBP and the simplified LBP was investigated. The proposed OCR system was evaluated on the off-line handwritten Bangla numeral database CMATERdb 3.1.1, and achieved an excellent accuracy of 96.7% character recognition rate.
Branch retinal vein occlusion (BRVO) is one of the most common retinal vascular diseases of the elderly that would dramatically impair one's vision if it is not diagnosed and treated timely. Automatic recognition ...
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ISBN:
(数字)9783319166285
ISBN:
(纸本)9783319166285;9783319166278
Branch retinal vein occlusion (BRVO) is one of the most common retinal vascular diseases of the elderly that would dramatically impair one's vision if it is not diagnosed and treated timely. Automatic recognition of BRVO could significantly reduce an ophthalmologist's workload, make the diagnosis more efficient, and save the patients' time and costs. In this paper, we propose for the first time, to the best of our knowledge, automatic recognition of BRVO using fundus images. In particular, we propose Hierarchical local binary pattern (HLBP) to represent the visual content of an fundus image for classification. HLBP is comprised of local binary pattern (LBP) in a hierarchical fashion with max-pooling. In order to evaluate the performance of HLBP, we establish a BRVO dataset for experiments. HLBP is compared with several state-of-the-art feature presentation methods on the BRVO dataset. Experimental results demonstrate the superior performance of our proposed method for BRVO recognition.
In this paper a generalized distance based local binary pattern (GDLBP) is proposed which achieves better recognition accuracy over LBP and significantly increases the recognition accuracy when combined with LVP. The ...
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ISBN:
(纸本)9781467385077
In this paper a generalized distance based local binary pattern (GDLBP) is proposed which achieves better recognition accuracy over LBP and significantly increases the recognition accuracy when combined with LVP. The proposed GDLBP is combined with local vector pattern (LVP) to achieve better recognition rates. Proposed GDLBP explores the spatial relationships of the neighboring pixels at different radii and inter radii distances. Proposed descriptors GDLBP and GDLBP-LVP have been analyzed and compared with the existing LBP and LVP over most challenging benchmark facial image databases LFW, and color FERET. Analysis of the descriptors shows that the proposed GDLBP and GDLBP-LVP achieves noticeably better results as compared to LBP and LVP.
In this paper, we propose a combined classifier approach based on Inner Distance Shape Context (IDSC) and local binary pattern (LBP) to classify shapes accurately. The inner-distance is insensitive to shape articulati...
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ISBN:
(纸本)9781450320023
In this paper, we propose a combined classifier approach based on Inner Distance Shape Context (IDSC) and local binary pattern (LBP) to classify shapes accurately. The inner-distance is insensitive to shape articulations and the LBP is invariant to rotation and shift of the shape. The Dynamic Programming (DP) in case of IDSC and Earth Movers Distance (EMD) metric in case of LBP were respectively employed to obtain similarity and hence used to classify given query shape based on maximum similarity value. The experiments are conducted on publicly available shape datasets namely MPEG-7, Kimia-99, Kimia-216, Myth and Tools-2D and the results are presented by means of Bulls eye score and precision-recall metric. The comparative study is also provided with the well known approaches to determine the retrieval accuracy of the proposed approach. The experimental results demonstrate that the proposed approach yield significant improvements over baseline shape matching algorithms.
Tracking-Learning-Detection (TLD) is an excellent visual tracking method, it decomposes the long-term tracking into three sub-tasks: tracking, learning and detecting. Each sub-task is addressed by a single component a...
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ISBN:
(纸本)9781467396752
Tracking-Learning-Detection (TLD) is an excellent visual tracking method, it decomposes the long-term tracking into three sub-tasks: tracking, learning and detecting. Each sub-task is addressed by a single component and operates simultaneously, all three sub-tasks are unified in a tracking-learning-detection framework. But our experiments show that it is sensitive to the illumination changing. In this paper, we try to improve its performance in such case by enhancing the nearest neighbor (NN) classifier with local binary pattern (LBP) algorithm. The modified NN classifier can get the bounding boxes which are closer to the tracking target. Moreover, the LBP algorithm has good performance on texture feature, so when the target has the good property of texture feature, the modified NN classifier has better performance. So a distinguish module is designed to select the right classifier. The experiments show that compared with conventional TLD algorithms, the proposed modification can improve the accuracy rate and robustness of the tracking results.
In this paper, the problem of video object detection in dynamic scene has been addressed. The dynamism is referred to the changes in the scene of interest, due to swaying of tree branches, leaves, fluctuation of surfa...
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ISBN:
(纸本)9788132220084
In this paper, the problem of video object detection in dynamic scene has been addressed. The dynamism is referred to the changes in the scene of interest, due to swaying of tree branches, leaves, fluctuation of surface in case of water bodies, variation of scene illumination, etc. The problem is formulated in a fixed camera scenario and with unavailability of reference frame (background model). The local binary pattern (LBP) is a very strong element used in object detection algorithms. In the literature, many methods exist, where the LBP histograms of current frame and previous frames are combined and used for background subtraction, to get the foreground detected. This histogram computation and construction of a final histogram for the background subtraction method is a very time-consuming and complex process. The complexity can be reduced to a large extent by using our proposed window-based LBP subtraction (WBLBPS) method. Moreover, the efficacy of the proposed method in terms of correct classification is quite satisfactory as compared to the other LBP-based methods.
local binary pattern (LBP) descriptors have been popular in texture classification in recent years. They were introduced as descriptors of local image texture and their histograms are shown to be well performing textu...
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ISBN:
(纸本)9781467380324
local binary pattern (LBP) descriptors have been popular in texture classification in recent years. They were introduced as descriptors of local image texture and their histograms are shown to be well performing texture features. In this paper we introduce two new LBP descriptors, alpha LBP and its improved variant I alpha LBP. We evaluate their performance in classification by comparing them with some of the existing LBP descriptors - LBP, ILBP, shift LBP (SLBP) and with one ternary descriptor - LTP. The texture descriptors are evaluated on three datasets - KTH-TIPS2b, UIUC and Virus texture dataset. The novel descriptor outperforms the other descriptors on two datasets, KTH-TIPS2b and Virus, and is tied for first place with ILBP on the UIUC dataset.
This article proposes a scheme for automatic extraction of text from scene images. First, we apply a color image segmentation algorithm to a scene image. To improve the color image segmentation performance, we incorpo...
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ISBN:
(纸本)9788132222477;9788132222460
This article proposes a scheme for automatic extraction of text from scene images. First, we apply a color image segmentation algorithm to a scene image. To improve the color image segmentation performance, we incorporate local binary pattern (LBP) and business features within it. local binary pattern (LBP) operator is a texture descriptor for grayscale images. On the other hand, the business feature describes the variation in intensity. The segmentation procedure separates out certain homogenous connected components from the image. We next inspect these connected components in order to identify possible text components. Here, we define a number of shape based features that distinguish between text and non-text connected components. Our experiments are based on the ICDAR 2011 Born Digital data set. The experimental results are satisfactory.
This paper presents an effective approach for the application of Face Recognition using local binary pattern operator. The face image is firstly divided in to the sub regions to generate the locally enhanced local Bin...
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ISBN:
(纸本)9781479999910
This paper presents an effective approach for the application of Face Recognition using local binary pattern operator. The face image is firstly divided in to the sub regions to generate the locally enhanced localbinary Histogram, which provide the features information on pixel level by creating LBP labels for histogram. Global localbinary Histogram for the entire face image is obtained by concatenating all the individual local histograms. As a pre-processing technique the differential excitation of pixel is used to make the algorithm invariant to the illumination changes. The performance of the algorithm is verified under constrains like pose, illumination and expression variation.
Automatic context recognition enables mobile devices to adapt their configuration to different environments and situations. This paper investigates the use of acoustic cues as a means of recognising context. The major...
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ISBN:
(纸本)9781479974504
Automatic context recognition enables mobile devices to adapt their configuration to different environments and situations. This paper investigates the use of acoustic cues as a means of recognising context. The majority of existing approaches exploit Mel-scaled cepstral coefficients (MFCCs) developed for the analysis of speech signals. The hypothesis in this paper is that new features are needed in order to capture complex acoustic structure. The paper introduces the use of local binary pattern (LBP) analysis which is used to complement MFCCs with acoustic texture information. The second contribution relates to a bag-of-features extension which clusters LBPs into a small number of codewords. Both approaches outperform the current state of the art and the latter is particularly appealing for embedded applications in which computational efficiency is paramount.
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