{"title":"基于MATLAB的可配置ATR系统设计","authors":"E. Wright, J. Northern","doi":"10.1109/TPSD.2008.4562741","DOIUrl":null,"url":null,"abstract":"Highly efficient and adaptable image recognition systems have become a high priority within the last decade due to terrorism. This paper describes a design and development process for a configurable automatic target recognition system. Our novel template matching system employs a Matlab algorithm developed to accurately detect object patterns within a JPEG image. After downloading the original JPEG image, the Matlab algorithm processes it in three sequential phases: 1) \"Sobel\" edge detection of the original image 2) Grey- level template matching based on the squared Euclidean distance theory and 3) Statistical pattern recognition of the resulting convoluted image. For our test cases, the cross correlation was determined using a template library created for the algorithm's image recognition process. The average runtime for our system ranges from 5-8 minutes, per test case, with 100% correct recognition using the template matching technique described.","PeriodicalId":410786,"journal":{"name":"2008 IEEE Region 5 Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of a Configurable ATR System Using MATLAB\",\"authors\":\"E. Wright, J. Northern\",\"doi\":\"10.1109/TPSD.2008.4562741\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Highly efficient and adaptable image recognition systems have become a high priority within the last decade due to terrorism. This paper describes a design and development process for a configurable automatic target recognition system. Our novel template matching system employs a Matlab algorithm developed to accurately detect object patterns within a JPEG image. After downloading the original JPEG image, the Matlab algorithm processes it in three sequential phases: 1) \\\"Sobel\\\" edge detection of the original image 2) Grey- level template matching based on the squared Euclidean distance theory and 3) Statistical pattern recognition of the resulting convoluted image. For our test cases, the cross correlation was determined using a template library created for the algorithm's image recognition process. The average runtime for our system ranges from 5-8 minutes, per test case, with 100% correct recognition using the template matching technique described.\",\"PeriodicalId\":410786,\"journal\":{\"name\":\"2008 IEEE Region 5 Conference\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE Region 5 Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TPSD.2008.4562741\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE Region 5 Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TPSD.2008.4562741","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Highly efficient and adaptable image recognition systems have become a high priority within the last decade due to terrorism. This paper describes a design and development process for a configurable automatic target recognition system. Our novel template matching system employs a Matlab algorithm developed to accurately detect object patterns within a JPEG image. After downloading the original JPEG image, the Matlab algorithm processes it in three sequential phases: 1) "Sobel" edge detection of the original image 2) Grey- level template matching based on the squared Euclidean distance theory and 3) Statistical pattern recognition of the resulting convoluted image. For our test cases, the cross correlation was determined using a template library created for the algorithm's image recognition process. The average runtime for our system ranges from 5-8 minutes, per test case, with 100% correct recognition using the template matching technique described.