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2022 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Archana, N. Menaka, R. Jothiraj, R. Kalidass, S. Chennai Institute of Technology Dept of Bio-Medical Engineering Chennai India Chennai Institute of Technology Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350333848

In today's society, there has been an increase in the number of suspicious and offensive behaviours. Many public areas, such as shopping malls, banks, and hotels, have CCTV cameras installed to safeguard the safety of their customers and visitors. CCTV cameras are now installed in many homes, particularly in those with single individuals and the elderly. Because human observation of these security cameras 24 hours a day, seven days a week is nearly impossible. It necessitates a workforce and their undivided attention in order to determine whether the collected activities are unusual or suspicious. As a result, this flaw necessitates the necessity to automate this procedure with high precision, as well as the ability to determine which frames and areas of the recording contain suspicious activity, allowing for faster judgment. As a result, we are using deep learning models for Intelligent Threat Recognition System to save time and labour waste. Its purpose is to detect signals of enmity in real time, allowing abnormalities to be distinguished from typical patterns. This method reports the technologies available in Open Computer Vision libraries and the methodologies to implement them using python. To provide the user friendly interface tkinter GUI interface is used. It uses haar cascade to detect the face and local binary pattern Algorithm to recognize the face. It also send a alert mail if it finds any uncommon activities. © 2022 IEEE.

关键词： local binary pattern

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A multi-channel framework based local binary pattern with two novel local feature descriptors for texture classification

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DIGITAL SIGNAL PROCESSING 2023年第1期140卷

作者： Lan, Shaokun Liao, Xuewen Fan, Hongcheng Hu, Shiqi Pan, Zhibin Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Air Force Engn Univ Inst Informat & Nav Xian 710077 Peoples R China AVIC Xian Flight Automat Control Res Inst Xian 710076 Peoples R China Xi An Jiao Tong Univ Res Inst Hangzhou 311215 Peoples R China

local binary pattern (LBP) has been widely used in various application fields, including object detection, texture analysis, and remote sensing. To improve discrimination performance, many LBP-based methods have been proposed for extracting different local feature information for texture classification. However, current LBP-based algorithms typically describe local features at a single sampling scale, but some significant and discriminative texture feature information is contained between different scales. Therefore, the lack of capability to extract cross-scale features can lead to losing critical texture features. Moreover, low-frequency texture information should be accorded great importance in texture classification. Additionally, if the diversity and validity of local feature extraction are lacking, the effectiveness of classification capability suffers. To address these issues, (1) we propose a completed cross-scale local binary pattern (ccsLBP) operator to extract cross-scale texture features. (2) A mean-filtered local binary pattern (LBPmf) operator is presented to highlight the important low-frequency texture information of texture images. (3) We build a high-performing multi-channel framework based local binary pattern (MC-LBP) for texture classification, which combines complementary features extracted by LBP, ccsLBP, and LBPmf hybridly to form a final feature vector of the texture image. The effectiveness of the proposed MC-LBP framework is verified on 6 representative texture databases, and the experimental results demonstrate its state-of-art texture classification performance.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： local binary pattern Cross-scale structure Mean filter Multi-channel framework Texture classification

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An edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy for local binary pattern

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 221卷

作者： Lan, Shaokun Fan, Hongcheng Hu, Shiqi Ren, Xincheng Liao, Xuewen Pan, Zhibin Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Informat & Commun Engn Xian 710049 Peoples R China Air Force Engn Univ Inst Informat & Nav Xian 710077 Peoples R China AVIC Xian Flight Automat Control Res Inst Xian 710076 Peoples R China Yanan Univ Sch Phys & Elect Informat Yanan 716000 Peoples R China

The local binary pattern (LBP) is a commonly used method for texture classification that performs well in terms of feature discrimination. However, (1) LBP can misclassify some important edge-located textures as non-uniform patterns with only one bin in the feature histogram, thus losing their discrimination capability. (2) When center pixel is contaminated by noise, a uniform pattern may be transformed into a non-uniform pattern, which can seriously affect the obtained LBP, thus degrading the classification results. To overcome these drawbacks, an edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy (SFB-OCPS) is proposed in this paper. To extract the correct edge pixels, we divide the whole texture image into 16 = 4 x 4 sub-images and propose an edge pixel selection strategy (EPSS) based on adaptive quantization with local threshold on each sub-image. Then 3 candidate center pixels constructed by statistical features of the local sampling neighborhood are generated for each edge-located center pixel. After the steps above, the SFB-OCPS strategy is introduced into the LBP-based algorithms. It is possible to recover some important edge-located non-uniform patterns to uniform patterns with an optimal center pixel selection, thus improving feature discrimination capability of the LBP-based algorithms. It should be emphasized that any LBP variants can introduce the proposed SFB-OCPS strategy to achieve the recovery of the edge-located uniform patterns. To validate the effectiveness of the proposed SFB-OCPS strategy, we introduce the SFB-OCPS strategy into the original LBP and 5 representative LBP-based algorithms. Experiments are conducted on 6 representative texture databases. Classification comparison reveals that the introduction of SFB-OCPS strategy can significantly improve the texture classification performance of LBP-based algorithms. Additionally, the noise-robustness of the proposed SFB-OCPS st

关键词： local binary pattern Statistical feature Selection strategy Optimal center pixel Edge pixel

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Classification of Parkinson's disease patients based on spectrogram using local binary pattern descriptors 1

Classification of Parkinson's disease patients based on spec...

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1st International Seminar on Physical Infrastructure, ISPI 2021

作者： Gelvez-Almeida, E. Vásquez-Coronel, A. Guatelli, R. Aubin, V. Mora, M. Laboratorio de Investigaciones Tecnologicas en Reconocimiento de Patrones Universidad Católica del Maule Talca Chile Facultad de Ciencias Básicas y Biomédicas Universidad Simón Bolívar San José de Cúcuta Colombia Departamento de Ingeniería e Investigaciones Tecnológicas Universidad Nacional de La Matanza Buenos Aires Argentina

Extreme learning machine is an algorithm that has shown a good performance facing classification and regression problems. It has gained great acceptance by the scientific community due to the simplicity of the model and its sola great generalization capacity. This work proposes the use of extreme learning machine neural networks to carry out the classification between Parkinson's disease patients and healthy individuals. The descriptor used corresponds to the feature vector generated applying the local binary pattern algorithm to the grayscale spectrograms. The spectrograms are obtained from the audio signal samples from the considered repository. Experiments are conducted with single hidden layer and multilayer extreme learning machine networks comparing the results of each structure. Results show that hierarchical extreme learning machine with three hidden layers has a better general performance over multilayer extreme learning machine networks and a single hidden layer extreme learning machine. The rate of success obtained is within the ranges presented in the literature. However, the hierarchical network training time is considerably faster compared to multilayer networks of three or two hidden layers. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： local binary pattern

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Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain

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Information Processing in Agriculture 2021年第4期8卷 581-592页

作者： Deepthy Mathew C.Sathish Kumar KAnita Cherian Department of Electronics and Communication Engineering Rajiv Gandhi Institute of TechnologyAPJ Abdul Kalam Technological UniversityKottayam 686501India Department of Electronics and Communication Engineering Government Engineering CollegeIdukki 685603India Department of Plant Pathology College of HorticultureKerala Agricultural UniversityThrissur 680656India

The fungal diseases in banana cause major yield losses for millions of farmers around the *** detection of these diseases helps the farmers to devise successful management *** characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each *** paper demonstrates a methodology for classification of three important foliar diseases in banana,using local texture *** disease affected regions are identified using image enhancement and color *** images are converted to transform domain using three image transforms(DWT,DTCWT and Ranklet transform).Feature vector is extracted from transform domain images using LBP and its variants(ELBP,MeanELBP and MedianELBP).These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation *** results showed best classification performance for ELBP features extracted from DTCWT domain(accuracy 95.4%,precision 93.2%,sensitivity 93.0%,Fscore 93.0%and specificity 96.4%).Compared with traditional methods of feature extraction,this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage.

关键词： Musa Plant disease classification Texture features local binary pattern DWT Image classifiers

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Person identification using novel local triangular binary pattern-based texture descriptor

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2025年第1期2025卷 1-14页

作者： Tekade, Arti Vijayan, T. Karthik, B. Mahajan, Anurag Bharath Inst Higher Educ & Res Dept Elect & Commun Engn Chennai India Pimpri Chinchwad Coll Engn & Res Coll Ravet Dept Elect & Tele Commun Pune India Symbiosis Int Symbiosis Inst Technol Dept Elect & Tele Commun Pune Campus Pune India

Human authentication is a crucial part of most computer vision automation systems. Conventional fingerprint, iris, face, or palm print-based systems cannot identify individuals when their external biometric components are destroyed, such as by severe burns, rashes, or wounds. The main elements of any person authentication system are non-forgery, security, resilience, and privacy. The local texture descriptor is vital in describing hand radiographic images' texture. This paper presents the novel local triangular binary pattern based texture descriptor to provide a local texture description of the hand radiographic images. The performance of the proposed descriptor is assessed using different machine learning classifiers such as K-nearest neighbor (KNN), support vector machine (SVM), radial basis function-SVM (RBF-SVM), classification tree (CT), and random forest (RF) for authentication of the 20 users based on hand radiographs. The suggested system provides an overall accuracy of 84.17% for KNN, 90% for SVM, 91.35% for RBF-SVM, 92.50% for CT, and 96.67% for RF for the 20 users for the In-house hand radiographic dataset.

关键词： Person authentication Forensic science Forensic hand radiography local binary pattern Support vector machine

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Improved Feature Extracting and Matching Algorithm Based on Fast and local binary pattern

SSRN

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SSRN 2024年

作者： Zhang, Zhen Zhu, Jianfang Zhang, Zhaoqi Wei, Jinzhan Wang, Jiyue Tang, Hongkuang School of Electronic Information and Automation Guilin University of Aerospace Technology Guangxi Guilin541004 China

This study introduces an enhanced version of the FAST algorithm aimed at achieving a uniform distribution of feature points. We also developed a feature descriptor based on local binary patterns (LBP), which provides rotation invariance and robust anti-noise capabilities. Furthermore, we have designed specific feature-matching algorithms tailored for these descriptors and created methods to identify and eliminate mismatched features. Finally, we thoroughly assess the algorithm'apos;s real-time performance and locating accuracy using a publicly available dataset. The experimental results demonstrate that our algorithm performs well in localization accuracy, with a running time that is approximately 10% of that of the SURF algorithm and just 1% of that required by the SIFT algorithm. © 2024, The Authors. All rights reserved.

关键词： local binary pattern

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Adaptive local binary pattern: A Novel Feature Descriptor for Enhanced Analysis of Kidney Abnormalities in CT Scan Images using ensemble based Machine Learning Approach

arXiv

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arXiv 2024年

作者： Hossain, Tahmim Sayed, Faisal Islam, Solehin University of Alberta Canada

The shortage of nephrologists and the growing public health concern over renal failure have spurred the demand for AI systems capable of autonomously detecting kidney abnormalities. Renal failure, marked by a gradual decline in kidney function, can result from factors like cysts, stones, and tumors. Chronic kidney disease may go unnoticed initially, leading to untreated cases until they reach an advanced stage. The dataset, comprising 12,427 images from multiple hospitals in Dhaka, was categorized into four groups: cyst, tumor, stone, and normal. Our methodology aims to enhance CT scan image quality using Cropping, Resizing, and CALHE techniques, followed by feature extraction with our proposed Adaptive local binary pattern (ALBP) feature extraction method compared with the state-of-the-art local binary pattern (LBP) method. Our proposed features fed into classifiers such as Random Forest, Decision Tree, Naive Bayes, K-Nearest Neighbor, and SVM. We explored an ensemble model with soft voting to get a more robust model for our task. We got the highest of more than 99% in accuracy using our feature descriptor and ensembling five classifiers (Random Forest, Decision Tree, Naive Bayes, K-Nearest Neighbor, Support Vector Machine) with the soft voting method. © 2024, CC BY-SA.

关键词： local binary pattern

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A Novel Approach of Fragile Watermarking for Authentication and Tamper Detection Exploiting local binary pattern (LBP) 5th

A Novel Approach of Fragile Watermarking for Authenticatio...

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5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Jana, Manasi Jana, Biswapati Joardar, Subhankar Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India

ISBN: (纸本)9783031488788

Data communication and transmission through internet has been rapidly increased now a days specially during and after COVID-19 pandemic situation. So, it is essential to preserve multimedia data from outlawed access. Here, a novel approach of fragile watermarking for authentication and tamper detection has been suggested. Firstly, an input image is split up into non-overlapping blocks of sized 3 × 3. A reference block has been chosen randomly to get the watermark which is hidden at the LSBs (Least Significant Bits) of the non-reference blocks of input image. The proposed scheme takes the advantage of local binary pattern (LBP) feature of the image which is used to generate the watermark. The proposed scheme maintains high quality watermarked images with 51.68 dB average PSNR and 0.9955 average SSIM. The proposed scheme has been analysed against different attacks like cropping and copy-move forgery which shows 100% Tamper Detection Rate (TDR) for cropping attack and 99.39% for copy-move forgery attack. The presented scheme also exceeds other methods with respect to PSNR and SSIM value as depicted in comparative analysis. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： local binary pattern

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Circular shift combination local binary pattern (CSC-LBP) method for dorsal finger crease classification

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第8期35卷

作者： Riaz, Imran Ali, Ahmad Nazri Ibrahim, Haidi USM Sch Elect & Elect Engn Engn Campus Nibong Tebal Malaysia Mirpur Univ Sci & Technol Mirpur Inst Technol Mirpur Azad Kashmir Pakistan

Biometric technology has drawn increasing attention and significance importance in recent years. In biometric security systems, personal identification and verification rely on their physical, behavioral, and biological characteristics. In this study, a new hand-based modality called dorsal finger creases is proposed for biometric classification. This modality is located on the dorsal surface of the finger, between the proximal knuckle and distal knuckle of the finger. However, it requires a specific feature extraction approach to extract the modality information on the selected region. Therefore, we have proposed a method for extracting the underlying features of the dorsal finger creases, called circular shift combination local binary pattern (CSC-LBP). The concept of CSC-LBP is to compute the local binary pattern within a 3 x 3 spatial window for each neighborhood pixel separately. Further, the concept of combination approach is applied on the individually computed eight LBP values to obtain the more discriminative feature vector. A multiclass support vector machine classifier is used for evaluating the effectiveness of the proposed CSC-LBP operator. Extensive experiments on self-collected datasets demonstrate the high classification accuracy and effectiveness of the proposed CSC-LBP method and confirm the usefulness of dorsal finger creases for personal recognition.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Dorsal finger creases local binary pattern Pixel average Feature extraction Support vector machine Classification

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