检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Zhang, Xin Jiang, Yanan Zhang, Chunlei Zhang, Zitong Department of Statistics and Epidemiology Graduate School of the PLA General Hospital Beijing100853 China School of Mathematical Sciences Beijing Normal University Beijing100875 China Beijing Zhongdi Runde Petroleum Technology Co. Ltd. Beijing100083 China School of Earth Sciences and Resources China University of Geosciences Beijing100083 China

The rich spatial and spectral information brings great potential for pixel-wise classification of hyperspectral image (HSI). Recently, local binary pattern (LBP) as a prominent texture operator has been introduced for discriminative feature extraction of complex volumetric HSI. However, the popular two-dimensional LBP series can only capture spatial structure features and lack pattern descriptions of the spectral domain. The extended three-dimensional LBP series are capable of local multidimensional descriptions, while the information mining for narrow and continuous bands in the spectral domain is still limited. To tackle these issues, we develop a novel local binary pattern named LatLBP for investigating spatial-spectral latent information, which provides a low-dimensional, fine-grained, high-level representation of dual-domain latent semantic information by exploring the group structure of data. LatLBP consists of representative spatial latent (RSLat) LBP encoding and grouped spectral latent (GSLat) LBP encoding, where RSLat describes the local spatial potential features of each grouped representative band, and GSLat characterizes information about each group of latent spectral factors. Besides, a supporting neighborhood band grouping (NBG) algorithm is also designed to provide a finer group structure. Extensive experimental results on four HSI datasets indicate that the proposed LatLBP operator is superior in feature dimension and classification accuracy compared to competitive methods. © 2023, The Authors. All rights reserved.

关键词： local binary pattern

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Edge extraction method for medical images based on improved local binary pattern combined with edge-aware filtering

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期74卷 103490-103490页

作者： Qiao, Shuang Yu, Qinghan Zhao, Zhengwei Song, Liying Tao, Hui Zhang, Tian Zhao, Chenyi Northeast Normal Univ Sch Phys Changchun 130024 Peoples R China Jilin Univ Bethune Hosp 1 Dept Hepatobiliary & Pancreat Surg Changchun Peoples R China Jilin Cent Gen Hosp Dept Oncol Integrated Chinese & Western Med Jilin 132011 Peoples R China Harbin Med Univ Affiliated Hosp 1 Dept Ophthalmol Harbin 150007 Peoples R China

Edge-based processing and analysis of medical images are indispensable in modern diagnosis and the application value of edge extraction technology is rising with this tide. For medical images containing redundant noise, blurred details, and low contrast, a robust edge extraction method based on edge-aware filtering and improved local binary pattern (EF-ALBP) is proposed in this paper. EF-ALBP contains two parts: the edge-aware filtering (EF) is proposed to suppress noise and enhance contrast while preserve edges, and ALBP is used to extract the crucial edge features of the previous step results accurately by introducing an accumulation function into local binary pattern. Quantitative analyses and visual evaluation for experimental results on X-ray, CT, and MRI images from medical image datasets demonstrate that the proposed method is competitive in robustness and outperforms those of the popular methods.

关键词： Edge extraction Medical images Edge-aware filtering local binary pattern Noise suppression

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An Improved local binary pattern Algorithm for Face Recognition Applications 1

An Improved Local Binary Pattern Algorithm for Face Recognit...

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1st IEEE Mysore Sub Section International Conference, MysuruCon 2021

作者： Shreedevi, P. Mohana, H.S. Malnad College of Engineering Dept. of Electronics and Instrumentation Engineering Hassan India Navkis College of Engineering Dept. of Electronics and Communication Engineering Hassan India

ISBN: (纸本)9780738146621

Security and authentication have become an essential part of the society. Face biometrics are one of the biometric security systems by researchers over the past years due to their ease of acquisition. Human face recognition is usually executed in two stages, Face detection and Face recognition. The main obj ective of this technique is either to ensure that the rendered services are accessed only by a legitimate user or to create a safety warning system in a given environment. In this paper, a novel face recognition system is designed using Haar-Cascade algorithm and efficient Improved local binary patterns Histograms (ILBPH) technique to identify a human face with an improved recognition rate. The results of the proposed algorithm show that it performs well in terms of processing time and training period. It also outperforms other existing algorithms with a recognition rate of about 96.4%. Therefore, the proposed method performs better in respect to temporal qualitative analysis. © 2021 IEEE.

关键词： local binary pattern

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CNN-Based Brain Tumor Detection Model Using local binary pattern and Multilayered SVM Classifier

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022年第0期2022卷 9015778页

作者： Kolla, Morarjee Mishra, Rupesh Kumar Ul Huq, S. Zahoor Vijayalata, Y. Gopalachari, M. Venu Siddiquee, KazyNoor-E-Alam Chaitanya Bharthi Inst Technol Dept Comp Sci & Engn Hyderabad Telangana India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool Andhra Pradesh India Gokaraju Rangaraju Inst Engn & Technol Dept Comp Sci & Engn Hyderabad Telangana India Chaitanya Bharthi Inst Technol Dept Informat Technol Hyderabad Telangana India Univ Sci & Technol Dept Comp Sci & Engn Chattogram Bangladesh

In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians;an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

关键词： local binary pattern

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Sleep arousal detection using one dimensional local binary pattern-based convolutional neural network

Sleep arousal detection using one dimensional local binary p...

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2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021

作者： Zan, Hasan Yildiz, Abdulnasir Mardin Artuklu University Vocational School Mardin Turkey Dicle University Department of Electrical and Electronics Engineering Diyarbakir Turkey

ISBN: (纸本)9781665436038

Sleep arousal is defined as a shift from deep sleep to light sleep or complete awakening. Arousals cause sleep deprivation by fragmenting sleep, and ultimately, many health problems. Arousals can be induced by well-studied apneas and hypopneas or other sleep orders such as hypoventilation, bruxism, respiratory effort-related arousals. Thus, detection of less-studied non-apnea/hypopnea arousals is important for diagnosis and treatment of sleep disorders. Traditionally, polysomnography (PSG) test that is recording and inspecting overnight physiological signals is used for sleep studies. In this work, a novel method based on one dimensional local binary pattern (1D-LBP) and convolutional neural network (CNN) for automatic arousal detection from polysomnography recordings is proposed. 25 recordings from PhysioNet Challenge 2018 PSG dataset are used for experiments. Each signal in PSG recordings is transformed to a new signal using 1D-LBP, and then segmented using 10-s-long sliding window. The segments are fed to a CNN model formed by stacking 25 layers for classification of non-apnea/hypopnea arousal regions from non-arousal regions. Area under precision-recall curve (AUPRC) and area under receiver operating characteristic curve (AUROC) metrics are used for performance measurement. Experimental results reflect that the proposed method shows a great promise and obtains an AUPRC of 0.934 and an AUROC of 0.866. © 2021 IEEE.

关键词： local binary pattern

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Block-Based local binary pattern for Recognition of Handwritten Odia Numerals 1

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International Conference on Communication, Circuits, and Systems, IC3S 2020

作者： Behera, Suchismita Das, Niva Bhubaneswar Odisha India

ISBN: (数字)9789813348660

ISBN: (纸本)9789813348653

In this paper, we exploit the texture feature of local binary pattern (LBP) for handwritten Odia numeral recognition. There are several challenges in the handwritten recognition due to the different writing style of the individuals. The histogram of the block-based LBP is computed considering the block sizes of (4 × 4), (8 × 8) and the whole image and these features are used to classify the numerals. For classification, SVM and single decision tree classifiers have been employed. In this experiment, we evaluated the performance of the block-based LBP on the ISI, Kolkata Odia numeral digital database. The performance of (4 × 4) block-based LBP shows high level of accuracy (97%) for both SVM and single tree classifiers. Single decision tree classifier shows promising results in terms of computational time and hence can be used for real-time applications. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： local binary pattern

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Hierarchical Multiscale local binary pattern for Better Osteoporosis Detection 1

Hierarchical Multiscale Local Binary Pattern for Better Oste...

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1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2021

作者： Mebarkia, Meriem Meraoumia, Abdallah Houam, Lotfi Khemaissia, Seddik Amel, Bouchemha Jennane, Rachid Tebessa Algeria University of Orleans I3MTO Laboratory France

ISBN: (纸本)9781665418560

Currently, a number of clinical decision tools have been developed for osteoporosis risk assessment by measuring bone mineral density and/or analyzing bone images. Unfortunately, the great similarity and correlation between healthy and diseased bone images represents the major obstacle in building an effective osteoporosis detection system, making this task a major challenge. The aim of this work is to develop a texture analysis based osteoporosis detection system. Hierarchical Multiscale local binary pattern (H-MLBP) technique is used in the feature extraction step. Thus, after selecting the dominant features, an emerging classifier, Ensemble Classifiers (EC), is used to classify them. The results obtained using a public database showed the robustness of the feature extraction method used to achieve high precision, which favors it to be effective for building an efficient osteoporosis detection system. © 2021 IEEE.

关键词： local binary pattern

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Seizure Detection with local binary pattern and CNN Classifier

Seizure Detection with Local Binary Pattern and CNN Classifi...

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2020 International E- Conference on Data Analytics, Intelligent Systems and Information Security, ICDIIS 2020

作者： Ramakrishnan, S. Muthanantha Murugavel, A.S. Sathiyamurthi, P. Ramprasath, J. Department of Information Technology Dr.Mahalingam College of Engineering and Technology Pollachi Tamilnadu India

The present work proposes two novel approaches namely One Dimensional adaptive average local binary pattern (1-D AaLBP) and One-Dimensional adaptive difference local binary pattern (1-D AdLBP) for feature extraction from EEG signals and Convolutional Neural Network (CNN) for classification of EEG signals. Both the proposed feature extraction methods are computationally easy to implement. In the first step the histograms are formed from the extracted patterns, after that feature vectors of the histogram are given as input to the classifier. Two benchmark EEG datasets such as Bonn and CHB-MIT are employed for conducting experiments for comparing the performances of the proposed method with other existing research works. The performance measures such as sensitivity, specificity, classification accuracy and execution time are used for evaluating the proposed methods. It is learned from the experiments conducted that among various methods the proposed method provides improved performance in terms of sensitivity, specificity, classification accuracy and execution time. © Published under licence by IOP Publishing Ltd.

关键词： local binary pattern

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local binary pattern and Census, Which One is Better in Stereo Matching 7

Local Binary Pattern and Census, Which One is Better in Ster...

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7th NAFOSTED Conference on Information and Computer Science, NICS 2020

作者： Nguyen, Vinh Dinh Nguyen, Phuc Hong Debnath, Narayan C. Eastern International University Department of Software Engineering Binh Duong Viet Nam

ISBN: (纸本)9780738105536

local binary patterns and Census share similar ideas of encoding the local region by establishing the relationship between neighbor pixels to obtain robust feature transformation. Recently, LBP and its variants have been successfully applied in various applications, such as texture classification, face recognition, object detection, and segmentation, while Census has only been used to investigate stereo correspondence problem. Therefore, this paper investigates the LBP and Census using a non-local-based stereo matching method in order to analyze and discuss the main differences between LBP and Census. Moreover, as many as one hundred variants of LBP have been published to solve various problems, while only a few modifications of the Census exist for stereo matching. Comprehensive experiments with the indoor, Middlebury dataset stated that some novel LBPs that perform well in texture classification and face recognition also work well in a stereo matching application. In most cases, LBP and its variants compare favorably to Census in terms of the accuracy of the stereo method. These results proved that LBP and its variants are suitable for using in solving the stereo correspondence problem or improving the performance of existing stereo methods. © 2020 IEEE.

关键词： local binary pattern

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local binary pattern based Technique for Content Based Image Copy Detection

Local Binary Pattern based Technique for Content Based Image...

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2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2020

作者： Srivastava, Mayank Siddiqui, Jamshed Ali, Mohammad Athar GLA University Department of CEA Mathura UP India Aligarh Muslim University Dept. of Computer Science Aligarh UP India University of Buckingham Dept. of Applied Computing Buckingham United Kingdom

ISBN: (数字)9781728165752

ISBN: (纸本)9781728165752

Now a day's there is a significant increase in the in the duplicate copies of large original images. One of the main reason for such duplication is due to the availability of large number of image editing software. In this paper, we have proposed a significant image hashing technique based on local binary pattern (LBP) for image identification. Here, the input image is initially pre-processed to remove any kind of minor effects. local binary pattern (LBP) is then applied to the processed image to produce features which is used for identification. The calculated experimental values show that proposed hashing is giving extraordinary performance against 'Histogram equalization' attack. Receiver operating characteristics (ROC) curve shows that proposed hashing gives better results as compared to other referenced techniques. Keeping in view the results obtained for the proposed technique, one can say that we can use such a copy detector for online detection of image copies. © 2020 IEEE.

关键词： local binary pattern

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