PATTERN RECOGNITION AND IMAGE ANALYSIS最新文献

{"title":"Image Inpainting by Machine Learning Algorithms","authors":"Qing Bu, Wei Wan, Ivan Leonov","doi":"10.1134/s1054661824700032","DOIUrl":"https://doi.org/10.1134/s1054661824700032","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Image inpainting is the process of filling in missing or damaged areas of images. In recent years, this area has received significant development, mainly owing to machine learning methods. Generative adversarial networks are a powerful tool for creating synthetic images. They are trained to create images similar to the original dataset. The use of such neural networks is not limited to creating realistic images. In areas where privacy is important, such as healthcare or finance, they help generate synthetic data that preserves the overall structure and statistical characteristics, but does not contain the sensitive information of individuals. However, direct use of this architecture will result in the generation of a completely new image. In the case where it is possible to indicate the location of confidential information on an image, it is advisable to use image inpainting in order to replace only the secret information with synthetic information. This paper discusses key approaches to solving this problem, as well as corresponding neural network architectures. Questions are also raised about the use of these algorithms to protect confidential image information, as well as the possibility of using these models when developing new applications.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"53 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552377","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}

{"title":"Algorithms of Isomorphism of Elementary Conjunctions Checking","authors":"T. Kosovskaya, Juan Zhou","doi":"10.1134/s1054661824010103","DOIUrl":"https://doi.org/10.1134/s1054661824010103","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>When solving artificial intelligence problems related to the study of complex structured objects, a convenient tool for describing such objects is the language of predicate calculus. The paper presents two algorithms for checking the isomorphism of pairs of elementary conjunctions of predicate formulas (they coincide up to variable names and the order of conjunctive terms). The first of the algorithms checks elementary conjunctions containing a single predicate symbol for isomorphism. Furthermore, if the formulas are isomorphic, it finds a one-to-one correspondence between the arguments of these formulas. If all predicates are binary, the proposed algorithm is an algorithm for checking two directed graphs for isomorphism. The second algorithm checks elementary conjunctions containing multiple predicate symbols for isomorphism. Estimates of their time complexity are given for both algorithms.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603108","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}

{"title":"AI-Based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities","authors":"Narek Papyan, Michel Kulhandjian, Hovannes Kulhandjian, Levon Aslanyan","doi":"10.1134/s1054661824010152","DOIUrl":"https://doi.org/10.1134/s1054661824010152","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly, enabling them to pinpoint potential locations where people might be trapped. Drones can cover larger areas in shorter timeframes compared to ground-based rescue efforts or even specially trained search dogs. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio “signatures.” Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) based on microphone array signals can enhance the precision of tracking the source of human noises.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"18 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590589","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}

{"title":"Unconditional Convergence of Sub-Gaussian Random Series","authors":"G. Giorgobiani, V. Kvaratskhelia, M. Menteshashvili","doi":"10.1134/s1054661824010061","DOIUrl":"https://doi.org/10.1134/s1054661824010061","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this paper we explore the basic properties of sub-Gaussian random variables and random elements. We also present various notions of subgaussianity (weak, <span>({mathbf{T}})</span>- and <span>({mathbf{F}})</span>-subgaussianity) of random elements with values in general Banach spaces. It is shown that the covariance operator of <span>({mathbf{T}})</span>-subgaussian random element is Gaussian and some consequences of this result in spaces possessing certain geometric properties are noted. Moreover, the almost sure (a.s.) unconditional convergence of random series are considered and a sufficient condition of a.s. unconditional convergence of a random series of a special type with values in a Banach space with some geometric properties is proved. By the a.s. unconditional convergence of random series we understand the convergence of all rearrangements of the series on the same set of probability 1. With some effort, we prove one of the main results of the paper, which gives us a necessary condition for the a.s. unconditional convergence of random series of a special type in a general Banach space. For the proof, a lemma is used that establishes a connection between the moments of a random variable and which may be of independent interest.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"47 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590707","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}

{"title":"An Enhanced Semi-Supervised Support Vector Machine Algorithm for Spectral-Spatial Hyperspectral Image Classification","authors":"Ziping He, Kewen Xia, Jiangnan Zhang, Sijie Wang, Zhixian Yin","doi":"10.1134/s1054661824010085","DOIUrl":"https://doi.org/10.1134/s1054661824010085","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Hyperspectral image classification has become an important issue in remote sensing due to the significant amount of spectral information in HSI. The costly and time-consuming annotation task of HSIs makes the number of labeled samples is limited. To address the above problem, we propose an enhanced semi-supervised support vector machine algorithm for spectral-spatial HSI classification. To fully capture the spectral and spatial information of HSI, we use local binary pattern to obtain spatial feature. The captured spatial features are concatenated with the spectral features to yield the hybrid spectral-spatial features. Self-training mechanism is then adopted to gradually select confident unlabeled samples with their pseudo-labels and add them to the labeled set. To further improve the classification performance of the semi-supervised support vector machine, we choose a cuckoo search algorithm based on the chaotic catfish effect to find its optimal combination of parameters. The experimental results on two publicly available HSI datasets show that the proposed model achieves excellent classification accuracy for each category in hyperspectral images, and also has superior overall accuracy compared with other comparative algorithms. Adequate experiments and analysis illustrate the promising potential and prospect of our proposed model for HSI classification.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"18 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590542","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}

{"title":"A Hierarchy of Determinative Sequent Systems with Different Substitution Rules","authors":"Hakob A. Tamazyan, Anahit A. Chubaryan","doi":"10.1134/s1054661824010206","DOIUrl":"https://doi.org/10.1134/s1054661824010206","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>A determinative sequent system DS for the classical propositional calculus is introduced on the base of well-known Tseitin’s transformation. It is proved that the system DS is polynomially equivalent to the propositional resolution system R and propositional cut-free sequent system PK^–. Then we define the system SDS (DS with a substitution rule) and the systems S_<i>k</i>DS (DS with restricted substitution rules, where the number of connectives in substituted formulas is bounded by <i>k</i>). It is proved that for every <i>k</i> ≥ 0 the system S_<i>k</i>+1DS has an exponential speed-up over the system S_<i>k</i>DS in the tree form, and the system SDS is polynomially equivalent to the Frege systems.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"39 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590543","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}

{"title":"Some Discrete Tomography Problems in Hypergraph Model Interpretation","authors":"Hasmik Sahakyan","doi":"10.1134/s1054661824010176","DOIUrl":"https://doi.org/10.1134/s1054661824010176","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this paper we consider discrete tomography problems with an additional requirement of non-repeatability of rows of the binary matrix to be reconstructed; as well as discrete tomography problems with given pairwise projections. Representing the problems in the hypergraph model and pointing out their equivalence to the basic definitions, we state the following results: (i) nonconvexity of the set of hypergraphic sequences of simple hypergraphs with <span>(n)</span> vertices and <span>(m)</span> hyperedges in the <span>(n)</span>-dimensional <span>(m + 1)</span>-valued lattice, (ii) characterization of monotone Boolean functions associated with degree sequences of 3-/(<i>n</i>–3)-uniform hypergraphs, (iii) formulation of discrete tomography problems with paired projections, their connection to hypergraph degree sequence problem with generalized degrees, a solution for a particular case.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"70 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590651","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}

{"title":"Complete Caps in Affine Geometry AG(n, 3)","authors":"Iskandar Karapetyan, Karen Karapetyan","doi":"10.1134/s1054661824010097","DOIUrl":"https://doi.org/10.1134/s1054661824010097","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>A cap in a projective or affine geometry over a finite field is a set of points no three of which are collinear. We give several new constructions for complete caps in affine geometry <span>(AGleft( {n,3} right))</span> over the field <span>({{F}_{3}} = left{ {0,1,2} right})</span> implying some well-known results.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"117 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590706","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}

{"title":"Logical-Probabilistic Modeling and Structural Analysis of Reconfigurable Systems Composed of Multifunctional Elements","authors":"Sergo Tsiramua, Hamlet Meladze, Tinatin Davitashvili","doi":"10.1134/s1054661824010218","DOIUrl":"https://doi.org/10.1134/s1054661824010218","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Multifunctional elements are a special class of elements, the reliability model of which differs from the classical two-pole “on-off” model. A multifunctional element can have partly false states in addition to nonfalse and false states. The multifunctionality of the elements leads to the formation of flexible, adaptable systems with reconfigurable structure, in which, in case of partial failure of the element, it is possible to continue the successful functioning of the system by redistributing the functions between the elements. In this paper, the properties of multifunctional elements (MFE) and systems, assembled on their basis, methods of structural analysis, logical-probabilistic reliability models and issues of optimal reconfiguration of systems based on MFE are discussed.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"263 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590535","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}

{"title":"Outer Bound for E-Capacity–Equivocation Region of Compound Wiretap Channel","authors":"Mariam Haroutunian","doi":"10.1134/s1054661824010073","DOIUrl":"https://doi.org/10.1134/s1054661824010073","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Investigation of communication over a wiretap channel is one of the problems of information–theoretic security. The aim in the general wiretap channel model is to maximize the rate of the reliable communication from the source to the legitimate receiver, while keeping the confidential information as secret as possible from the wiretapper (eavesdropper). Here we consider the compound wiretap channel model, when the channels to the legitimate receiver and to the wiretapper depends on the number of possible states. We investigate the <span>(E)</span>-capacity–equivocation region which is the closure of the set of all achievable rate-reliability and equivocation pairs, where the rate-reliability function represents the optimal dependence of rate on the error probability exponent (reliability). Here the outer bound of this region is constructed in the case, when the states are not known to all terminals.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"77 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602804","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}