{"title":"应用级联方法作为通用物体检测工具","authors":"","doi":"10.1134/s1054661823040302","DOIUrl":null,"url":null,"abstract":"<span> <h3>Abstract</h3> <p>This paper is devoted to a review of the achievements of the Moscow scientific school of image recognition, formed under the leadership of Professor Vladimir L’vovich Arlazarov, in the field of development and application of the Viola–Jones method. One of the main areas of research at the school is the development of computationally efficient recognition algorithms, which requires a deep understanding of the problem and a wide expertise in the field of existing classical algorithms. Such classic method as the Viola—Jones method became an essential tool to solve a wide range of image recognition problems. This paper provides an overview of the modifications of the original method developed by the scientific school and describes in detail the experience of solving many different practical problems that arise in the development of modern energy-efficient image recognition systems.</p> </span>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"303 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Cascade Methods as a Universal Object Detection Tool\",\"authors\":\"\",\"doi\":\"10.1134/s1054661823040302\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<span> <h3>Abstract</h3> <p>This paper is devoted to a review of the achievements of the Moscow scientific school of image recognition, formed under the leadership of Professor Vladimir L’vovich Arlazarov, in the field of development and application of the Viola–Jones method. One of the main areas of research at the school is the development of computationally efficient recognition algorithms, which requires a deep understanding of the problem and a wide expertise in the field of existing classical algorithms. Such classic method as the Viola—Jones method became an essential tool to solve a wide range of image recognition problems. This paper provides an overview of the modifications of the original method developed by the scientific school and describes in detail the experience of solving many different practical problems that arise in the development of modern energy-efficient image recognition systems.</p> </span>\",\"PeriodicalId\":35400,\"journal\":{\"name\":\"PATTERN RECOGNITION AND IMAGE ANALYSIS\",\"volume\":\"303 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PATTERN RECOGNITION AND IMAGE ANALYSIS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1134/s1054661823040302\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PATTERN RECOGNITION AND IMAGE ANALYSIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s1054661823040302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Application of Cascade Methods as a Universal Object Detection Tool
Abstract
This paper is devoted to a review of the achievements of the Moscow scientific school of image recognition, formed under the leadership of Professor Vladimir L’vovich Arlazarov, in the field of development and application of the Viola–Jones method. One of the main areas of research at the school is the development of computationally efficient recognition algorithms, which requires a deep understanding of the problem and a wide expertise in the field of existing classical algorithms. Such classic method as the Viola—Jones method became an essential tool to solve a wide range of image recognition problems. This paper provides an overview of the modifications of the original method developed by the scientific school and describes in detail the experience of solving many different practical problems that arise in the development of modern energy-efficient image recognition systems.
期刊介绍：
The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
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