PATTERN RECOGNITION AND IMAGE ANALYSIS最新文献

Construction of a Semiautomatic Contour of Areal Objects on Hyperspectral Satellite Images 在高光谱卫星图像上构建半自动等值线的区域物体

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700111

Bin Lei, Wei Wan, Artiom Nedzved, Alexei Belotserkovsky

引用次数: 0

Some Scientific Results of the 16th International Conference PRIP-2023 第 16 届 PRIP-2023 国际会议的部分科学成果

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700019

S. V. Ablameyko, I. B. Gurevich, A. M. Nedzved, V. V. Yashina

引用次数: 0

Automation of Eye Disease Diagnoses Using Descriptive Image Algebras and Boolean Algebra Methods 利用描述性图像代数和布尔代数方法实现眼病诊断自动化

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700093

I. B. Gurevich, V. V. Yashina

{"title":"Automation of Eye Disease Diagnoses Using Descriptive Image Algebras and Boolean Algebra Methods","authors":"I. B. Gurevich, V. V. Yashina","doi":"10.1134/s1054661824700093","DOIUrl":"https://doi.org/10.1134/s1054661824700093","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The article presents an algebraic model for solving the problem of automation of ophthalmological diagnostics written in the language of descriptive image algebras. Descriptive image algebras are an initial mathematical language for formalizing and standardizing representations and procedures for processing image models and conversions over them when extracting information from images. To construct an algebraic model for solving the problem of automation of ophthalmological diagnostics, descriptive algebras of images with one ring are mainly used. This class of algebras belongs to the class of universal linear algebras with a sigma-associative ring with identity. A series of conversions and steps of the algebraic model are described using descriptive Boolean algebras over images. Descriptive image algebras are the main section of the mathematical apparatus of descriptive image analysis, which is a logically organized set of descriptive methods and models designed for image analysis and evaluation. The article defines specialized versions of descriptive image algebras with one ring and descriptive Boolean algebras over images, over models and representations of images, and over conversions of image models and images themselves, necessary for constructing an algebraic model. The image models (representations, formalized descriptions) used in writing the article are described. An example of a descriptive algorithmic scheme for solving an applied ophthalmological problem using an algebraic model is constructed.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Scientific Gateway for Evaluating Land-Surface Temperatures Using Landsat 8 and Meteorological Data over Armenia and Belarus 利用大地遥感卫星 8 和亚美尼亚及白俄罗斯气象数据评估地表温度的科学途径

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700020

R. Abrahamyan, A. Belotserkovsky, P. Lukashevich, A. Gevorgyan, H. Grigoryan, H. Astsatryan

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Crowd Movement Type Estimation in Video by Integral Optical Flow and Convolution Neural Network 利用积分光流和卷积神经网络估计视频中的人群运动类型

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700068

Huafeng Chen, Angelina Pashkevich, Shiping Ye, Rykhard Bohush, Sergey Ablameyko

{"title":"Crowd Movement Type Estimation in Video by Integral Optical Flow and Convolution Neural Network","authors":"Huafeng Chen, Angelina Pashkevich, Shiping Ye, Rykhard Bohush, Sergey Ablameyko","doi":"10.1134/s1054661824700068","DOIUrl":"https://doi.org/10.1134/s1054661824700068","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The paper proposes a new approach for crowd movement type estimation in video by combining convolutional neural network and integral optical flow. At first, main notions of crowd detection and tracking are given. Secondly, crowd movement features and parameters are defined. Three rules are proposed to identify direct crowd motion. Signs are presented for identifying chaotic crowd movement. Region movement indicators are introduced to analyze the movement of a group of people or a crowd. Thirdly, an algorithm of crowd movement types estimation using convolutional neural network and integral optical flow is proposed. We calculate crowd movement trajectories and show how they can be used to analyze behavior and divide crowds into groups of people. Experimental results show that with the help of convolutional neural network and integral optical flow crowd movement parameters can be calculated more accurately and quickly. The algorithm demonstrates stronger robustness to noise and the ability to get more accurate boundaries of moving objects.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Random Search in Neural Networks Training 神经网络训练中的随机搜索

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s105466182470010x

V. V. Krasnoproshin, V. V. Matskevich

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A Writer-Dependent Approach to Offline Signature Verification Based on One-Class Support Vector Machine 一种基于单类支持向量机的离线签名验证方法

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700135

V. V. Starovoitov, U. Yu. Akhundjanov

{"title":"A Writer-Dependent Approach to Offline Signature Verification Based on One-Class Support Vector Machine","authors":"V. V. Starovoitov, U. Yu. Akhundjanov","doi":"10.1134/s1054661824700135","DOIUrl":"https://doi.org/10.1134/s1054661824700135","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>A new solution to the problem of offline signature verification is presented. Digital images of signatures are processed and converted into a binary representation of a certain size. Then their contours are traced, and from them, two original features are calculated for describing the local structural features of the signature in the form of vectors of normalized frequency distributions of local binary pattern codes and values of local curvature of the signature contours. A new feature space is formed in which the pattern describes the proximity of pairs of signatures, and its coordinates are the rank correlation coefficients between the feature vectors of these signatures. In real practice, the expert has <i>M</i> (from 5 to 15) genuine signatures of a person; there are no forged signatures at all. On these <i>M</i> available genuine signatures of a single person, we train a one-class support vector machine model and obtain a single-writer-dependent classifier. A verifiable signature is considered forged if the classifier model considers it to be an outlier. The accuracy of our approach in verifying the genuineness of all 2640 signatures from the CEDAR database was 99.77%. All forged signatures in this database were correctly recognized.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection System of Landscape’s Unnatural Changes by Satellite Images Based on Local Areas 基于本地区域的卫星图像景观非自然变化检测系统

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700159

Xi Zhou, Qing Bu, Vadim Vladimirovich Matskevich, Alexander Mixailovich Nedzved

引用次数: 0

Monitoring of Egg Growing in Video by the Improved DeepLabv3+ Network Model 用改进的 DeepLabv3+ 网络模型监测视频中的鸡蛋生长过程

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700081

Fengyang Gu, Hui Zhu, Haiyang Wang, Yanbo Zhang, Fang Zuo, S. Ablameyko

引用次数: 0

Identification of Mutation Combinations in Genome-Wide Association Studies: Application for Mycobacterium tuberculosis 识别全基因组关联研究中的突变组合：结核分枝杆菌的应用

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI: 10.1134/s1054661824700044

Yu-Xiang Chen, A. M. Andrianov, A. V. Tuzikov

{"title":"Identification of Mutation Combinations in Genome-Wide Association Studies: Application for Mycobacterium tuberculosis","authors":"Yu-Xiang Chen, A. M. Andrianov, A. V. Tuzikov","doi":"10.1134/s1054661824700044","DOIUrl":"https://doi.org/10.1134/s1054661824700044","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In genome-wide association studies, combinations of single nucleotide polymorphisms are considered to be more effective than individual mutations in linking genes to traits. Clearly, finding the most relevant combinations from tens of thousands of these mutations associated with a trait is a complicated combinatorial problem. To achieve the higher prediction performance, improve computational efficiency and results interpretation, we proposed three algorithms for searching combinations of individual mutations and applied these algorithms to 3178 samples of <i>Mycobacterium tuberculosis</i> strains for predicting their drug resistance to 20 drugs. The single nucleotide polymorphisms associated with drug resistance were identified in the <i>Mycobacterium tuberculosis</i> genome using the single-marker test, and the combinations of individual mutations were searched using the multimarker test. The data were compared with those predicted by the widely recognized Mykrobe and TB-profiler software. Comparative analysis of the results obtained showed that, excepting for ofloxacin, the combinations of individual mutations found by our algorithms for the second-line drugs have some advantages in prediction accuracy.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0