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检索条件"主题词=Local binary patterns"

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Combination of LBP and ESRC for Single Sample Infrared and Visible Face Fusion Recognition

Combination of LBP and ESRC for Single Sample Infrared and V...

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International Conference on Image and Video Processing, and Artificial Intelligence (IVPAI)

作者： Xie, Zhihua Jiangxi Sci & Technol Normal Univ Key Lab Opt Elect & Commun Nanchang Jiangxi Peoples R China

ISBN: (数字)9781510623118

ISBN: (纸本)9781510623118

Near infrared and visible fusion recognition is an active topic for robust face recognition. local binary patterns (LBP) based descriptors and sparse representation based classification (SRC) become two significant techniques in face recognition. In this paper, near infrared and visible face fusion recognition based on LBP and extended SRC is proposed for single sample problem. Firstly, the local features are extracted by LBP descriptor for infrared and visible face representation. Secondly, the extend SRC (ESRC) is applied for single sample problem. Finally, to get a robust and time-efficient fusion model for unconstrained face recognition with single sample situation, the infrared and visible features fusion problem is resolved by error-level fusion based on ESRC. Experiments are performed on HITSZ LAB2 database and the experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition with single sample situation.

关键词： local binary patterns Sparse Representation based Classification Error-level fusion Face fusion recognition

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local binary PATTERN WITH RANDOM FOREST FOR ACOUSTIC SCENE CLASSIFICATION

LOCAL BINARY PATTERN WITH RANDOM FOREST FOR ACOUSTIC SCENE C...

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IEEE International Conference on Multimedia and Expo (IEEE ICME)

作者： Abidin, Shamsiah Xia, Xianjun Togneri, Roberto Sohel, Ferdous Univ Western Australia Sch Elect Elect & Comp Engn Perth WA Australia Murdoch Univ Sch Engn & Informat Technol Perth WA Australia

ISBN: (纸本)9781538617373

This paper presents an approach for acoustic scene classification using the local binary pattern (LBP) and random forest (RF). The audio signal is converted to a Constant-Q transform (CQT) representation and LBP is used to extract the features from this time-frequency representation. The CQT representations are divided into a number of sub-bands to obtain more localized features relevant to the spectral information. We then use random forest to select the most important features for each band of extracted LBP features. For further performance enhancement, we use feature level fusion of LBP and HOG features. The proposed system has achieved an accuracy of 85% on the DCASE 2016 dataset.

关键词： acoustic scene local binary patterns feature extraction time-frequency analysis fusion random forest

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A Palmprint Classification Method based on local Sparse Coding 4

A Palmprint Classification Method based on Local Sparse Codi...

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IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yang, Dongliang Song, Changjiang Wu, Gang Heilongjiang Acad Sci Inst Automat Harbin Heilongjiang Peoples R China Heilongjiang Acad Sci Inst Adv Technol Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538653739

A palmprint classification method based on local sparse coding is presented. Firstly, patch-wise statistics based local binary patterns features are as input of local sparse coding classifier. And then the method finds k samples, which are similar with the test sample. Finally the sparse representation classifier is applied to the test sample and the k samples to determine the classification information of the test sample in the database. The experimental results on PolyU palmprint database show that new method has higher classification accuracy than the tradition methods.

关键词： palmprint recognition sparse representation k-nearest neighbor local binary patterns

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Classification of the Air Quality Level based on Analysis of the Sky Images

Classification of the Air Quality Level based on Analysis of...

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Iranian Conference on Electrical Engineering

作者： M. M. Samsami N. Shojaee S. Savar M. Yazdi Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran School of Electrical and Computer Engineering Shiraz University Shiraz Iran

Air pollution is one of the world's most substantial environmental health risks, and efforts are being made to reduce its adverse impacts on human health. The prime step in preventing the threats posed by air pollution is devising tools for reporting the air quality to the public by which they are able to make proper decisions to reduce the exposure to the pollution. In this paper, we present an image-processing-based method for classification of the air quality level (AQL) by leveraging on image analytics and computer vision techniques. By analyzing an image taken of the sky and extracting useful features from it, the proposed method can accurately classify the AQL and is suitable to be embedded into portable devices such as smartphones to inform the users of the air pollution exposure status. A dataset of 3422 images, called ImSkySet, was built and used to extract features for training the classifier. We have thoroughly evaluated the performance of the system and reported the experimental results which show an average accuracy of approximately 82.02%. Our results provide a promising case for the benefits of utilizing image processing approaches to monitor the air quality for the purpose of raising public awareness.

关键词： Air quality level Air pollution Image processing Gabor filter local binary patterns Dark channel

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Facial Expression Recognition via Kernel Sparse Representation

Facial Expression Recognition via Kernel Sparse Representati...

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2019 International Conference on Applied Mathematics, Modeling, Simulation and Optimization(AMMSO 2019)

作者： Shi-ping CHEN Xiao-hu WANG Zhi-qiang PENG Dong-chu CHEN Jun HONG School of Electrical and Information Engineering Hunan Institute of Technology

For seeking sparse representation coefficients in the kernel feature space, we propose a novel method of facial expression recognition based on kernel sparse representation classification to improve the recognition rate of facial expression. The 1 l-norm minimization problem was solved in this paper. The coefficient vector was obtained and the local binary patterns features were extracted as the facial representation features. kernel sparse representation classification was used to perform facial expression classification and compared with sparse representation-based classification, the nearest subspace, support vector machines, K-nearest neighbor, and radial basis function neural network. Test results based on the universal JAFFE database show that the presented kernel sparse representation classification method is more effective than other methods in facial expression recognition.

关键词： Facial expression recognition Kernel sparse representation Feature extraction local binary patterns

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A study of some fast inpainting methods with application to iris reconstruction 22

A study of some fast inpainting methods with application to ...

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22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Ignat, Anca Vasiliu, Alexandru Univ Alexandru Ioan Cuza Iasi Fac Comp Sci 16 Berthelot Str Iasi 700483 Romania

The human iris is an important biometrical element that can be successfully used for person identification. Because the image acquisition process does not provide complete information of the iris, this paper studies the problem of partial iris recognition. For texture characterization of the iris we used local binary patterns, Dual Tree Complex Wavelet Transform and Daugman's IrisCode. The evaluation of the results was made using the Equal Error Rate ( EER) and AUC (Area Under the ROC Curve). The missing parts of the iris images were filled-in using patch-based inpainting. We then studied the effect and results on the iris recognition process of these inpainting methods. By using simple texture features and optimizing the inpainting method we achieved faster computations with very good recognition results. The methods were tested on UPOL, a well-known iris dataset. Our conclusion is that the inpainting process yields better results than those obtained for occuluded irises. (C) 2018 The Authors. Published by Elseiver Ltd.

关键词： iris recognition inpainting local binary patterns IrisCode

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Detecting coffee (Coffea arabica L.) sequential flowering events based on image segmentation 7

Detecting coffee (<i>Coffea arabica</i> L.) sequential flowe...

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7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Peng, Huaiyue Huang, Jingfeng Jin, Hongwei Sun, Han Chai, Dengfeng Wang, Xiuzhen Han, Bing Zhou, Zhen Xu, Libing Zhejiang Univ Inst Appl Remote Sensing & Informat Technol Hangzhou Zhejiang Peoples R China Jiangsu Prov Radio Sci Inst Co Ltd Wuxi Jiangsu Peoples R China Dept Xintianyuan Inst Disaster Prevent & Reduct Hohhot Peoples R China Zhejiang Univ Inst Spatial Informat Technol Hangzhou Zhejiang Peoples R China Hangzhou Normal Univ Inst Remote Sensing & Earth Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538650394

Coffee sequential flowering events detection and flower density estimation is essential to predict the ripening time and yield of coffee. In this study, we detected coffee flowering events automatically based on estimated flower densities in high spatial time-series digital images, using a multi-scale region based flower segmentation method. The study area is a coffee plantation in Lujiangba in the Yunnan Province of China. There are 5 flowering events in the coffee flowering period. A digital camera obtained 24 RGB images at each shooting time of day (8:00, 9:00, 10:00, 11:00, 12:00, 13:00, 14:00, 15:00, 16:00, 17:00, 17:30) by automatic adjusting the sensor with 3 depression angles and 8 azimuth angles during the coffee flowering period from March 1st to May 31st. For segment the flowers in one image, multi-scale regions were primarily generated by equally-sized superpixel segmentation and subsequent superpixel merging process. Next, the feature vectors of each region were extracted by color moments (CM) operator and local binary patterns (LBP) operator. Afterwards, the support vector machine (SVM) classifier trained on these features was applied to recognize the flower regions. Thus, the percentage of flower pixels referred as flower proportion (FP), which can estimate the image-based flower density, was calculated in preparation for detecting flowering events of time-series images. In this stage, coefficients of Recall, Precision and intersection over union (IoU) were employed to evaluate the performance of segmentation methods on 14 test images at threes depression angles and then the best flower segmentation algorithm and the optimal angle can be determined. In the stage of flowering events detection, the FPs of different shooting time multi temporal images under the optimal depression angle were calculated and plotted. Then, a threshold of FP, K, was selected to determine whether an image is on a flowering day. To determine the best shooting time for flowerin

关键词： coffee flowering events superpixel merging flower segmentation color moment local binary patterns support vector machine

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Fusion of Geometric and Texture Features For Ear Recognition 2

Fusion of Geometric and Texture Features For Ear Recognition

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2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Jiddah, Salman Mohammed Yurtkan, Kamil Cyprus Int Univ Comp Engn Haspolat Cyprus

ISBN: (纸本)9781538641842

The field of ear recognition has been getting a lot of attention in recent years in biometric recognition systems field, which have seen a rise in interest in the present day, not only because it provides a better substitute for conventional identification and authentication systems, but also because of the high rise in global insecurities which makes the biometric research field a very active research field. The aim of our study is to contribute to the field of ear recognition by applying both texture and geometric features. This research in respect to its aims and objectives carried out experiments on AMI ear database by extracting the local binary patterns features, and also applying Laplacian filter on the raw images separately to extract the geometric features. The ear database was processed by dividing ear images into four quarters and experimented on them individually in a pursuit of identifying the region of high interest in human ear images. Texture and geometric features are then fused and experiments were carried out on to verify the contribution of the fused features.

关键词： local binary patterns Laplacian filter ear recognition biometric pattern recognition image histogram

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local binary Pattern and Tchebychev Moments as a Single Image Discriptor 3

Local Binary Pattern and Tchebychev Moments as a Single Imag...

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3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Lamine, Benrais Nadia, Baha Univ Sci & Technol USTHB Dept Comp Sci Algiers Algeria

ISBN: (纸本)9781538642382

This paper experiences a novel approach of object description using a combination of two well-known descriptors "LBP: local binary patterns" and "DTMs: Discrete Tchebychev Moments". With their pros and cons, the LBP and Tchebychev are widely and successfully used in the computer vision community. LBP is a local based descriptor while the DTMs is a global based descriptor seem to makes the combination difficult, however, we managed in this paper to propose an interesting approach that takes advantage of their pros while imitating their cons and outperform their weaknesses. The proposed approach is tested on the COIL dataset and return very interesting results going up to 89,98% of well classified objects.

关键词： Tchebychev Moments local binary patterns Object Description Object Classification

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A Robust HER2 Neural Network Classification Algorithm Using Biomarker-Specific Feature Descriptors 20

A Robust HER2 Neural Network Classification Algorithm Using ...

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20th IEEE International Workshop on Multimedia Signal Processing (MMSP)

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

ISBN: (纸本)9781538660706

Computer assisted evaluations of Whole Slide Images (WSI) of histopathological slides require robust biomarker-specific feature descriptors for accurate grading and classification. Considering the large amount of processing involved in analysing WSIs, training and classification, it is important to have an optimized set of features that closely represent the characteristics of the biomarkers used by pathologists in manual assessments. In this paper, we consider the problem of classifying WSIs of ImmunoHistoChemistry (IHC) stained slides for automated breast cancer grading. We use a combination of intensity and texture features derived from the input image at different saturation levels, and show its effectiveness in a Neural Network architecture for classifying the image into one of the four HER2 scores. The paper also presents three configurations for the neural network and gives comparative analysis showing the variations of classification accuracy with respect to changes in the configuration and the learning rate.

关键词： Automated HER2 Scoring local binary patterns Characteristic Curves Whole Slide Image Analysis Neural Networks HER2 Classification

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