Boris Kovalerchuk, Michael Kovalerchuk, S. Streltsov, M. Best
{"title":"特征提取以解决不确定性的指导","authors":"Boris Kovalerchuk, Michael Kovalerchuk, S. Streltsov, M. Best","doi":"10.1117/12.2016509","DOIUrl":null,"url":null,"abstract":"Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides.","PeriodicalId":338283,"journal":{"name":"Defense, Security, and Sensing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Guidance in feature extraction to resolve uncertainty\",\"authors\":\"Boris Kovalerchuk, Michael Kovalerchuk, S. Streltsov, M. Best\",\"doi\":\"10.1117/12.2016509\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides.\",\"PeriodicalId\":338283,\"journal\":{\"name\":\"Defense, Security, and Sensing\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Defense, Security, and Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2016509\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Defense, Security, and Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2016509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Guidance in feature extraction to resolve uncertainty
Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides.