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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Oguz, Oguzhan Toker, Kemal Gurkan Dobrucali, Oguzcan Cakir, Serdar Tubitak Bilgem ILTAREN Ankara Turkey

ISBN: (纸本)9781538615010

In this paper, in order to track UAV's and maritime targets an algorithm which is based on extraction of local binary patterns (LBP) near salient points is proposed. The method extracts the LBP histograms from the regions near the detected salient points. Later these histograms are concatenated and used for object representation. The tracking is achieved by comparing the main histogram with the histograms extracted inside the search region. The proposed method is tested for both UAV and sea surface targets and experimental results are presented.

关键词： Target Tracking Shi-Tomasi local binary patterns UAV

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DETECTION OF DIABETIC RETINOPATHY AND AGE-RELATED MACULAR DEGENERATION FROM FUNDUS IMAGES THROUGH local binary patterns AND RANDOM FORESTS

DETECTION OF DIABETIC RETINOPATHY AND AGE-RELATED MACULAR DE...

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IEEE International Conference on Image Processing (ICIP)

作者： Morales, Sandra Engan, Kjersti Naranjo, Valery Colomer, Adrian Univ Politecn Valencia Inst Interuniv Invest Bioingn & Tecnol Orientada LabHuman I3BH Camino de Vera S-N E-46022 Valencia Spain Grp Tecnologias Informat Aplicadas Oftalmol Unidad Conjunta UPV FISABIO Madrid Spain Univ Stavanger Dept Elect Engn & Comp Sci N-4036 Stavanger Norway

ISBN: (纸本)9781479983391

This work focuses on differentiating between pathological and healthy fundus images. The goal is to distinguish between diabetic retinopathy (DR), age-related macular degeneration (AMD) and normal images by analysing the texture of the retina background. local binary patterns (LBP) are used as texture descriptors. The two class problems DR vs. normal and AMD vs. normal, as well as the three class problem of DR, AMD, and normal, have been tested and have obtained promising results. An average sensitivity and specificity higher than 0.86 in all the cases and almost of 0.96 for AMD detection were achieved with a random forest classifier. These results suggest that LBP is a robust texture descriptor for retinal images and the method proposed in this paper, analysing the retina background directly and avoiding difficult lesion segmentation, can be useful for diagnostic aid.

关键词： local binary patterns Diabetic Retinopathy Age-related Macular Degeneration Fundus Image Retina Texture

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local binary patterns of Spatial Plane and Temporal Linear for Spotting Spontaneous Expression Frames 2019

Local Binary Patterns of Spatial Plane and Temporal Linear f...

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Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

作者： Xiaofeng Fu Jun Wu Xiaojuan Fu School of Computer Science and Technology Hangzhou DianZi University Hangzhou China Department of Basic Qinghai Communications Technical College Xining China

ISBN: (纸本)9781450376273

Spontaneous expression can reveal people's true emotions as comparing with traditional expression. Spotting spontaneous expression frames in the video is prerequisite for studying its characteristics. This paper proposes the local binary patterns feature extraction algorithm based on spatial plane and temporal line. Firstly, the improved normalized cross-correlation algorithm is used to finely match the eye region and the mouth region respectively. Then, fusing two kinds of features, one is the mean local binary patterns features of linear region which are extracted from the temporal linear, the other is the local binary patterns features of the sector region which are extracted from the spatial plane. Finally, the feature is converted into the frame feature by feature correlation function, and frame feature of the spontaneous expression frame is larger than threshold. The experimental results show that our algorithm performs well on the CAS(ME)2 database. The hitting rate of negative spontaneous expression segments increases by 27% than the ULBP algorithm. Simultaneously, the AUC value of the ROC curve increases by 1% as comparing to the LBP-TOP algorithm.

关键词： Spontaneous expression videos local binary patterns Spotting Spatial plane Temporal linear

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Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

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JOURNAL OF ELECTRONIC IMAGING 2018年第1期27卷 011002-011002页

作者： Bianconi, Francesco Bello-Cerezo, Raquel Napoletano, Paolo Univ Perugia Dept Engn Perugia Italy Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy

Texture classification plays a major role in many computer vision applications. local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intraand interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images. (c) 2017 SPIE and IS&T

关键词： local binary patterns image classification color texture convolutional neural networks

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Sub Oriented Histograms of local binary patterns for Smoke Detection and Texture Classification

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2016年第4期10卷 1807-1823页

作者： Yuan, Feiniu Shi, Jinting Xia, Xue Yang, Yong Fang, Yuming Wang, Rui Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330032 Jiangxi Peoples R China Jiangxi Agr Univ Vocat Sch Teachers & Technol Nanchang 330032 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Sch Software & Commun Engn Nanchang 330032 Jiangxi Peoples R China

local binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.

关键词： Sub Oriented Histogram Smoke detection Image Classification local binary patterns Support Vector Machine

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Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis

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Procedia Computer Science 2019年 159卷 1375-1386页

作者： Tobechukwu Agbele Blessing Ojeme Richard Jiang CAIR Department of Computer Science University of Cape Town Rondebosh 7701 and CAIR South Africa

The paper describes the application of local binary patterns and cascade AdaBoost classifier (CAC) to detect and analyse mice behavioural movement. This was done with a view to investigating the inconsistencies associated with current practices, whereby mice behavioural classification is achieved by means of human-generated labels. The developed cascade AdaBoost algorithm was able to detect eight different mice movement, and we develop a system that allows mice behavioural analysis in videos, with minimal supervision. Evaluating the results on Completeness, Consistency and Correctness, and based on the devised analysis, a solution was deployed, showing that machine learning plays an important role in translating video data into scientific knowledge. This is a useful addition to the animal behaviourist’s analytical toolkit.

关键词： computer vission machine learning mice behavioural pattern detection local binary patterns cascade AdaBoost classifier

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CONSECUTIVE GENDER AND AGE CLASSIFICATION FROM FACIAL IMAGES BASED ON RANKED local binary patterns

COMPUTER OPTICS

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COMPUTER OPTICS 2015年第5期39卷 762-769页

作者： Rybintsev, A. V. Konushin, V. S. Konushin, A. S. Moscow MV Lomonosov State Univ Computat Math & Cybernet Fac ASVK Dept Graph & Multimedia Lab Moscow Russia Video Anal Technol LLC Moscow Russia Higher Sch Econ Moscow Russia

A new algorithm for consecutive classification of gender and age based on a two-stage support vector regression is proposed. Only most significant local binary patterns are used to describe the image. To enhance the gender classification accuracy we use bootstrapping with the training based on difficult examples, whereas the age classification is improved through the use of floating age ranges.

关键词： machine learning image classification gender classification age classification local binary patterns Adaboost support vector machine bootstrapping support vector regression

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Image Features Based on Characteristic Curves and local binary patterns for Automated HER2 Scoring

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JOURNAL OF IMAGING 2018年第2期4卷 35-35页

作者： Mukundan, Ramakrishnan Univ Canterbury Dept Comp Sci & Software Engn Christchurch 8140 New Zealand

This paper presents novel feature descriptors and classification algorithms for the automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC)-stained slides. The first descriptor is called a characteristic curve, which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is a local binary pattern (LBP) feature curve, which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and a comparative analysis.

关键词： medical image classification local binary patterns characteristic curves whole slide image processing automated HER2 scoring

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Comparative Study on local binary patterns for Mammographic Density and Risk Scoring

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JOURNAL OF IMAGING 2019年第2期5卷 24-24页

作者： George, Minu Zwiggelaar, Reyer Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (local binary Pattern), ELBP (Elliptical local binary Pattern), Uniform ELBP, LDP (local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical local binary Pattern descriptors and local Directional patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region.

关键词： breast density classification risk estimation local binary patterns texture descriptors

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Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using local binary patterns

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JOURNAL OF IMAGING 2018年第1期4卷 3-3页

作者： Singh, Prerna Mukundan, Ramakrishnan De Ryke, Rex Univ Canterbury Dept Comp Sci & Software Engn Christchurch 8041 New Zealand Canterbury Dist Hlth Board Serv Radiol Canterbury 8140 New Zealand

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modeling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

关键词： ultrasound image analysis speckle noise synthetic ultrasound images texture features local binary patterns image quality assessment

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