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On Analyzing Various Density Functions of local binary patterns for Optic Disc Segmentation

On Analyzing Various Density Functions of Local Binary Patte...

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IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE)

作者： Mohamed, Nur Ayuni Zulkifley, Mohd Asyraf Hussain, Aini Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43000 Malaysia

ISBN: (纸本)9781479989690

In building an automated glaucoma detection system, optic disc segmentation is the first step that needs to be implemented follows by optic cup segmentation in order to quatify the severity level of glaucoma. Glaucoma is an ocular eye disease that can lead to gradual vision loss and permanent blindness if it is not treated in the early stage. Many glaucoma patients are unaware of their disease since they rarely encounter any symptom that can lead to glaucoma. Thus, detecting glaucoma during the early stage is very important to reduce the treatment risk. This paper proposes optic disc segmentation by using local binary patterns operator (LBP), a feature for textural classification in image processing. LBP is utilized only on red channel of RGB fundus image because of higher contrast between optic disc and its surrounding area compared to the blue and green channels. Smoothing technique, specifically, histogram equalization is performed to improve the quality of input image before LBP method is applied. Lastly, morphological operation and filtering are applied to filter out the artifacts and remove the noise from the segmented image. RIM-One database is used to validate the simulation results with Exponential distribution achive better performance with average accuracy and precision of 0.8951 and 0.7390 respectively.

关键词： local binary patterns Optic disc segmentation Glaucoma Textural classification

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Features Representation by Multiple local binary patterns for Facial Expression Recognition 11

Features Representation by Multiple Local Binary Patterns fo...

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11th World Congress on Intelligent Control and Automation

作者： Wang, Li Li, Ruifeng Wang, Ke Harbin Inst Technol Dept State Key Lab Robot & Syst Harbin Heilongjiang Pr Peoples R China

ISBN: (纸本)9781479958252

To recognize expressions conveniently and effectively, an enhanced feature representation method is proposed for facial expression recognition. local binary pattern histogram Fourier (HF-LBP) features is used to represent facial expression features. Multiple HF-LBP features are extracted to form recognition vectors for facial expression recognition in the approach, which include sign and magnitude LBP in the completed LBP scheme with multiple radii and different size neighborhoods to achieve enough features. It represents images from different scales and directions in the local neighborhood by overall considerations from the aspect. K-nearest neighborhoods classifier is applied for expression recognition after representing facial features using HF-MLBP. Comparisons are made with other extension LBP operators to evaluate the approach. The experimental results show that our method has good performance in facial expression recognition.

关键词： local binary patterns feature Fourier transform multiple features facial expression recognition

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Edge-based Foreground Detection with Higher Order Derivative local binary patterns for Low-resolution Video Processing

Edge-based Foreground Detection with Higher Order Derivative...

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9th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Deboeverie, Francis Allebosch, Gianni Van Haerenborgh, Dirk Veelaert, Peter Philips, Wilfried UGent iMinds Dept Telecommun & Informat Proc Image Proc & Interpretat St Pietersnieuwstr 41 B-9000 Ghent Belgium

ISBN: (纸本)9789897581335

Foreground segmentation is an important task in many computer vision applications and a commonly used approach to separate foreground objects from the background. Extremely low-resolution foreground segmentation, e.g. on video with resolution of 30x30 pixels, requires modifications of traditional high-resolution methods. In this paper, we adapt a texture-based foreground segmentation algorithm based on local binary patterns (LBPs) into an edge-based method for low-resolution video processing. The edge information in the background model is introduced by a novel LBP strategy with higher order derivatives. Therefore, we propose two new LBP operators. Similar to the gradient operator and the Laplacian operator, the edge information is obtained by the magnitudes of First Order Derivative LBPs (FOD-LBPs) and the signs of Second Order Derivative LBPs (SOD-LBPs). Posterior to background subtraction, foreground corresponds to edges on moving objects. The method is implemented and tested on low-resolution images produced by monochromatic smart sensors. In the presence of illumination changes, the edge-based method outperforms texture-based foreground segmentation at low resolutions. In this work, we demonstrate that edge information becomes more relevant than texture information when the image resolution scales down.

关键词： Foreground Segmentation Edge Detection local binary patterns Low-resolution Video Processing

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Illumination Invariant Face Recognition with Whitening Filter and local binary patterns

Illumination Invariant Face Recognition with Whitening Filte...

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6th International Symposium on Computational Intelligence and Design (ISCID)

作者： Li, Shun Long, Fei Cheng, Xuefeng Xiamen Univ Software Sch Xiamen Peoples R China

ISBN: (纸本)9780769550794

In this paper we present an approach for the illumination invariant face recognition, which combines 2D spatial whitening filter with the LBP descriptor. Firstly, we preprocess each face image by applying whitening filter in the logarithm domain. Then LBP features are extracted based on the filtered images. After representing image with a LBP operator, the image will be divided into non-overlapped blocks and histograms are used to represent each block. Finally the concatenated histograms are used for recognition. The experimental results on the Yale B and Extended Yale B prove the outperformance of the proposed method compared to other existing methods.

关键词： Face Recognition Whitening Filter local binary patterns

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Face Recognition under Varying Illumination based on Homomorphic Filter and local binary patterns

Face Recognition under Varying Illumination based on Homomor...

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Intelligent Systems Conference (IntelliSys)

作者： Alotaibi, Shorouq Alharbi, Nouf Kurdi, Heba King Saud Univ Coll Comp & Informat Sci Comp Sci Dept Riyadh Saudi Arabia

ISBN: (纸本)9781509064359

Facial recognition is one of the most significant biometric techniques. As a wide research field, it is faced with several challenges;the biggest of which is illumination variations. To overcome this challenge, several various techniques for facial recognition systems have been proposed by researchers. This paper is to improve recognition accuracy when detecting faces in illumination variations. To accomplish that, a face detector to recognize faces under various illuminations was implemented. The proposed model, (H-LBP), utilizes a combination of two phases: preprocessing and features extraction. During preprocessing, homomorphic filtering was used, and during features extraction, local binary patterns was used. The homomorphic filter was used to lower the effect of illumination that comes from an input image. local binary patterns were used to eliminate illumination variations. The results proved that the combination of different phases was an effective choice since a better accuracy level was achieved than when each of the phases (preprocessing and extraction) was separately used.

关键词： Face recognition illumination variations preprocessing features extraction homomorphic filter local binary patterns

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Classification of Cell Types on Histopathological Images Using local binary patterns

Classification of Cell Types on Histopathological Images Usi...

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Medical Technologies Congress (TIPTEKNO)

作者： Ozer, Hatice Sumeyye Demir, Ceyda Bilgin, Gokhan Yildiz Tekn Univ Bilgisayar Muhendisligi Bolumu TR-34220 Istanbul Turkey

ISBN: (纸本)9781728124209

In recent years, the use of computer aided diagnostic (CAD) systems has been increasing with a high acceleration in the field of digital pathology. Application and study areas are expanding over time include the detection, classification and segmentation of nuclei. In this study, various traditional machine learning methods (k-closest neighborhood, random forests and support vector machines) and deep learning (convolutional neural network) were used comparatively on CRC colorectal adenocarcinomas dataset. Since conventional machine learning algorithms do not receive a two-dimensional input such as convolutional neural network, local binary images are utilized. As a result, when the feature extraction for machine learning algorithms is performed, KNN and RF algorithms provide very successful results, whereas CNN algorithm gave better results without making any feature extraction.

关键词： Hisopathological images convolutional neural network classification local binary patterns

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Palmprint recognition based on discriminative local binary patterns statistic feature

Palmprint recognition based on discriminative local binary p...

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2nd International Conference on Signal Acquisition and Processing

作者： Mu, Meiru Ruan, Qiuqi Shen, Yongsheng Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

ISBN: (纸本)9780769539607

In on-line palmprint recognition tasks, texture based feature extraction methods are widely adopted for palmprint representation, owing to their high performance. Gabor filter bank is among the most promising texture information extraction tool because of its multi-scale and multi-orientational characteristics. However, it is both time and memory intensive to convolve palm images with a bank of filters to extract features. In this paper, a novel palmprint texture representation is proposed, discriminative local binary patterns statistic (DLBPS), which is extracted for palmprint recognition. In this approach, a palmprint is firstly divided into non-overlapping and equal-sized regions, which are then labeled into local binary patterns (LBP) independently. By calculating these patterns' distribution, the statistic features of the palmprint texture are attained. Subsequently, the Discriminative Common Vectors (DCV) algorithm is applied for dimensionality reduction of the feature space and solution of the optional discriminative common vectors. Finally, Euclidean distance and the nearest neighbor classifier are used for palmprint classification. Our experimental results demonstrate the effectiveness of the proposed DLBPS palmprint representation, which brings both high recognition accuracy rate and high speed benefits.

关键词： Palmprint recognition local binary patterns Discriminative Common Vector

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A new speaker verification spoofing countermeasure based on local binary patterns

A new speaker verification spoofing countermeasure based on ...

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14th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2013)

作者： Alegre, Federico Vipperla, Ravichander Amehraye, Asmaa Evans, Nicholas EURECOM Multimedia Commun Dept Sophia Antipolis France Nuance Commun Cambridge England

ISBN: (纸本)9781629934433

This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed, converted voice signals. The new countermeasure is based on the analysis of a sequence of acoustic feature vectors using local binary patterns (LBPs). Compared to existing approaches the new countermeasure is less reliant on prior knowledge and affords robust protection from not only voice conversion, for which it is optimised, but also spoofing attacks from speech synthesis and artificial signals, all of which otherwise provoke significant increases in false acceptance. The work highlights the difficulty in detecting converted voice and also discusses the need for formal evaluations to develop new countermeasures which are less reliant on prior knowledge and thus more reflective of practical use cases.

关键词： speaker verification biometrics imposture countermeasures local binary patterns

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Human Activity Recognition Based On Silhouette Analysis Using local binary patterns

Human Activity Recognition Based On Silhouette Analysis Usin...

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10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Su, Han Zou, Jiayun Wang, Wenjie Sichuan Normal Univ Key Lab Virtual Real & Visual Comp Sichuan Chengdu 610066 Peoples R China Sichuan Normal Univ Coll Comp Sci Chengdu 610066 Peoples R China

ISBN: (纸本)9781467352536

In this paper, we present a novel method for activity analysis in outdoor environment. Difficulties, such as low resolution, shadows, long distances, and segmentation problems, usually exist in outdoor environment. To deal with these difficulties, the activity width sequence image which maintains 3D features using 2D representation is exploited to represent the silhouette structure information of each frame and dynamic properties of activity. The activity width vectors are converted to the gray value successively according to the order in activity sequence and the gray image is formed in spatio-temporal space. We regard the activity width sequence image as the texture and choose local binary patterns which is a powerful texture extraction operator to analyze the spatio-temporal pattern. The method is simple and effective. We test our method based on the outdoor database and the results are encouraging.

关键词： Human Activity Recognition local binary patterns Silhouette Width

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Improving the Face Recognition Accuracy under Varying Illumination Conditions for local binary patterns and local Ternary patterns based on Weber-Face and Singular Value Decomposition 3

Improving the Face Recognition Accuracy under Varying Illumi...

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3rd International Conference on Green Technology and Sustainable Development (GTSD)

作者： Chi-Kien Tran Tseng, Chin-Dar Lee, Tsair-Fwu Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung Taiwan Hanoi Univ Ind Hanoi Vietnam Kaohsiung Med Univ Grad Inst Clin Med Kaohsiung 807 Taiwan

ISBN: (纸本)9781509036387

This paper addresses a new approach based on the Weber-face and singular value decomposition (SVD) methods to improve the recognition accuracy for a face recognition system using local binary patterns and local ternary patterns in an illumination variation environment. The face images are the first extracted illumination-invariant components by the Weber-face method. Secondly, SVD is applied to the encoded images. Next, the training encoded images are extracted features based on local binary patterns or local ternary patterns. Finally, in the classification phase, the singular value matrix of a test image is combined with those of the training images to adjust the illumination of the test image before the features are extracted and classified. The recognition is performed using a nearest neighbor classifier with Chi-square as a dissimilarity measure. Experimental results on the extended Yale B database demonstrated the efficiency of our proposed method. Thus, the proposed approach is expected to contribute to the face recognition problem under varying illumination conditions.

关键词： face recognition illumination pretreatment Weber-face singular value decomposition local binary patterns local ternary patterns

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