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IEEE 7th International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Zhao, Zhiqiang Zheng, Qiaoli Zhang, Yan Shi, Xin Chongqing Univ Posts & Telecommun Sch Bioinformat Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Optoelect Engn Chongqing 400065 Peoples R China

ISBN: (纸本)9781728106410

Since head pose estimation is influenced by illumination variation, expression, noise disturbance and other factors, which results in low rate of recognition, a method of head pose estimation based on multi-feature fusion is proposed in this paper. At first, a pose feature combining the second-order histogram of oriented gradient (HOG) and the uniform pattern of local binary pattern (UP-LBP) is proposed, which is used for head pose estimation in single image. Then, an improved random forest algorithm is adopted for classification of head pose and solving the instability problem of the algorithm. Finally, the improved random forest classifier is used for head pose estimation of the novel pose feature. The experimental results show that, the method proposed in this paper is more capable of classification and with better robustness to illumination variation.

关键词： head pose estimation histogram of oriented gradient local binary patterns random forest

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Towards Automated Multiscale Imaging and Analysis in TEM: Glomerulus Detection by Fusion of CNN and LBP Maps 15th

Towards Automated Multiscale Imaging and Analysis in TEM: Gl...

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15th European Conference on Computer Vision (ECCV)

作者： Wetzer, Elisabeth Lindblad, Joakim Sintorn, Ida-Maria Hultenby, Kjell Sladoje, Natasa Uppsala Univ Uppsala Sweden Karolinska Inst Solna Sweden Vironova AB Stockholm Sweden Serbian Acad Arts & Sci Math Inst Belgrade Serbia

ISBN: (纸本)9783030110246;9783030110239

Glomerulal structures in kidney tissue have to be analysed at a nanometer scale for several medical diagnoses. They are therefore commonly imaged using Transmission Electron Microscopy. The high resolution produces large amounts of data and requires long acquisition time, which makes automated imaging and glomerulus detection a desired option. This paper presents a deep learning approach for Glomerulus detection, using two architectures, VGG16 (with batch normalization) and ResNet50. To enhance the performance over training based only on intensity images, multiple approaches to fuse the input with texture information encoded in local binary patterns of different scales have been evaluated. The results show a consistent improvement in Glomerulus detection when fusing texture-based trained networks with intensitybased ones at a late classification stage.

关键词： Glomerulus detection Transmission Electron Microscopy Convolutional Neural Networks local binary patterns Digital pathology

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Quantitative Ore Texture Analysis with Convolutional Neural Networks 18th

Quantitative Ore Texture Analysis with Convolutional Neural ...

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18th IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing (MMM)

作者： Fu, Y. Aldrich, C. Western Australia Sch Mines GPO Box U1987 Kalgoorlie WA 6844 Australia

Liberation of valuable minerals from their gangue matrices depends on the physical properties of the ore, as well as the features of the process used to extract these minerals from the rock particulates. Despite the fact that the texture of the ore is an important predictor of liberation in an ore system, it is only recently that quantitative descriptors of the texture have been included in liberation models. These descriptors can be obtained in a variety of ways, but a general methodology has not yet been established. In this study, recent advances in quantitative ore texture analysis are reviewed and the feasibility of using state-of-the-art computer vision technology based on convolutional neural networks for ore texture analysis is considered. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： ore texture multivariate image analysis local binary patterns convolutional neural network deep learning ResNet mineral processing

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Spatio-Temporal Texture Features for Presentation Attack Detection in Biometric Systems 8

Spatio-Temporal Texture Features for Presentation Attack Det...

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8th International Conference on Emerging Security Technologies (EST)

作者： Pan, Shi Deravi, Farzin Univ Kent Canterbury Kent England

ISBN: (纸本)9781728155463

Spatio-temporal information is valuable as a discriminative cue for presentation attack detection, where the temporal texture changes and fine-grained motions (such as eye blinking) can be indicative of some types of spoofing attacks. In this paper, we propose a novel spatio-temporal feature, based on motion history, which can offer an efficient way to encapsulate temporal texture changes. patterns of motion history are used as primary features followed by secondary feature extraction using local binary patterns and Convolutional Neural Networks, and evaluated using the Replay Attack and CASIA-FASD datasets, demonstrating the effectiveness of the proposed approach.

关键词： Presentation Attack Detection Facial Biometrics local binary patterns Convolutional Neural Networks Spatio-temporal Features

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A Low-Budget Approach for Vehicle Detection and Occlusion Removal on Traffic Videos 26

A Low-Budget Approach for Vehicle Detection and Occlusion Re...

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26th International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Silva, Luis F. V. Bandeira, Danilo R. C. Carvalho, Bruno M. Fed Inst Rio Grande Norte Currais Novos Brazil Univ Fed Rio Grande do Norte Natal RN Brazil

ISBN: (纸本)9781728132273

A huge increase in traffic flow in large cities has created serious problems for urban planning and transit authorities, as they seek for solutions for handling increasingly larger traffic jams. In this paper, we describe part of an automated system designed to extract information from videos captured by closed-circuit surveillance camera systems to perform traffic monitoring. The main contributions of this paper are a combination of known methods for performing robust vehicle detection and counting as well as the adaptation of the Viola and Jones framework to deal with occlusion events. We tested our system on videos acquired at different times of the day and the results show a dear improvement over the traditional Viola and Jones method. We also compared the results of two versions of our approach to YOLO V3, a state-of-the-art method for detecting and classifying objects, on three different computers, and achieved slightly lower occlusion solving rates. We emphasize that YOLO V3 worked at very low FPS rates for one computer when not using the GPU, when compared to the Frames Per Second (FPS) rates achieved by our approach. Moreover, YOLO could not be executed on the notebook used in the experiments or a low-budget device Raspberry Pi 2, while our approaches achieved processing rates between 8 and 10 FPS on the Raspberry Pi 2 model.

关键词： Haar features local binary patterns occlusion handling traffic flow YOLO

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Recognition of Transrectal Ultrasound Prostate Image Based on HOG-LBP 13

Recognition of Transrectal Ultrasound Prostate Image Based o...

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13th IEEE International Conference on Anti-Counterfeiting, Security, and Identification (ASID)

作者： Huang, Xiaofu Chen, Ming Liu, PeiZhong Huaqiao Univ Coll Engn Quanzhou Peoples R China Fujian Med Univ Zhangzhou Affiliated Hosp Zhangzhou Peoples R China Quanzhou Zhongfang Hongye Informat Technol Co Ltd Quanzhou Peoples R China Fujian Prov Big Data Res Inst Intelligent Mfg Quanzhou Peoples R China

ISBN: (纸本)9781728124582

Prostate biopsy is a gold standard for diagnosing prostate cancer. In clinic, multi-needle saturation puncture is often used in the diagnosis of prostate cancer. Although it can improve the positive rate of diagnosis, it also increases the probability of postoperative infection, hematuria and other complications. This paper presents a method to identif prostate cancer by histogram of oriented gradient (HOG) and local binary pattern (LBP) feature extraction. Firstly, Gaussian filtering, gradient transformation function and other algorithms are used to preprocess the transrectal ultrasound prostate images to filter out image noise and improve contrast. Then, the local and global texture feature information of the image is extracted by using HOG and LBP. Finally, support vector machine (SVM) is used to classify features and identify positive regions. The results show that the proposed method is superior to other methods. The transrectal ultrasound prostate images exhibit superior diagnostic performance with an accuracy of 72.2% and a specificity of 75%. Experiments show that this method can provide the necessary auxiliary information for doctor diagnosis and reduce the number of puncture needles.

关键词： puncture biopsy prostate cancer histogram oriented gradient local binary patterns

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Strawberry Detection Under Various Harvestation Stages

Strawberry Detection Under Various Harvestation Stages

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作者： Fitter, Yavisht California Polytechnic State University

学位级别：M.Sc., Master of Science

This paper analyzes three techniques attempting to detect strawberries at various stages in its growth cycle. Histogram of Oriented Gradients (HOG), local binary patterns (LBP) and Convolutional Neural Networks (CNN) were implemented on a limited custom-built dataset. The methodologies were compared in terms of accuracy and computational efficiency. Computational efficiency is defined in terms of image resolution as testing on a smaller dimensional image is much quicker than larger dimensions. The CNN based implementation obtained the best results with an 88% accuracy at the highest level of efficiency as well (600x800). LBP generated moderate results with a 74% detection accuracy at an inefficient rate (5000x4000). Finally, HOG’s results were inconclusive as it performed poorly early on, generating too many misclassifications.

关键词： Histogram of Oriented Gradients local binary patterns Convolutional Neural Networks

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Research on Face Recognition Algorithms and Application Based on PCA Dimension Reduction and LBP

Research on Face Recognition Algorithms and Application Base...

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作者： Kangman Li Ruihua Nie College of Computer Science and Technology Hengyang Normal University

In the face recognition system on campus,the influence of time and age change on face features can be *** paper proposes a dimension reduction algorithm based on Principal Component Analysis(PCA) algorithm and local binary patterns(LBP),And it is applied to campus face recognition *** is proved that the algorithm can significantly improve the speed and ensure its recognition accuracy in the application of small changes in the face,and has a certain reference value for practical application.

关键词： Face recognition PCA local binary patterns

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local fractal dimension and binary patterns in texture recognition

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PATTERN RECOGNITION LETTERS 2016年 78卷 22-27页

作者： Florindo, Joao B. Bruno, Odemir M. Univ Sao Paulo Sao Carlos Inst Phys POB 369 BR-13560970 Sao Carlos SP Brazil

The present work proposes a new texture image descriptor, combining the local binary patterns extracted from the grey-level image (classic approach) with those extracted from the local fractal dimension at each point of the image. In this way, these descriptors express two important measurements from the image, i. e., the variation among pixel intensities in each local neighbourhood and the local complexity (pixel arrangement) at each point. Such combination provides a rich and robust descriptor even for the most complex textures. The effectiveness of the proposed solution is evaluated in the classification of two well-known benchmark databases: UIUC and USPTex, showing that the combined features outperform all the other compared approaches in terms of correctness rates in the classification of grey-scale texture images. (C) 2016 Elsevier B.V. All rights reserved.

关键词： local fractal dimension local binary patterns Texture recognition Image analysis

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On the role and the importance of features for background modeling and foreground detection

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COMPUTER SCIENCE REVIEW 2018年 28卷 26-91页

作者： Bouwmans, Thierry Silva, Caroline Marghes, Cristina Zitouni, Mohammed Sami Bhaskar, Harish Frelicot, Carl Univ La Rochelle Lab MIA La Rochelle France Movidius Timisoara Romania Khalifa Univ Visual Signal Anal & Proc VSAP Res Ctr Dept Elect & Comp Engn Abu Dhabi U Arab Emirates

Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have been proposed recently, no traditional algorithm today still seem to be able to simultaneously address all the key challenges of illumination variation, dynamic camera motion, cluttered background and occlusion. This limitation can be attributed to the lack of systematic investigation concerning the role and importance of features within background modeling and foreground detection. With the availability of a rather large set of invariant features, the challenge is in determining the best combination of features that would improve accuracy and robustness in detection. The purpose of this study is to initiate a rigorous and comprehensive survey of features used within background modeling and foreground detection. Further, this paper presents a systematic experimental and statistical analysis of techniques that provide valuable insight on the trends in background modeling and use it to draw meaningful recommendations for practitioners. In this paper, a preliminary review of the key characteristics of features based on the types and sizes is provided in addition to investigating their intrinsic spectral, spatial and temporal properties. Furthermore, improvements using statistical and fuzzy tools are examined and techniques based on multiple features are benchmarked against reliability and selection criterion. Finally, a description of the different resources available such as datasets and codes is provided. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Background modeling Foreground detection Features local binary patterns

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