{"title":"迭代无监督目标检测系统","authors":"S. Onis, Henri Sanson, Christophe Garcia","doi":"10.1109/IWSSIP.2008.4604450","DOIUrl":null,"url":null,"abstract":"Current object detection systems provide good results, at the expense of requiring a large training database. This paper presents an unsupervised iterative object detection system using a selection of previously detected objects in order to perform new object detection. Our experiments show that this method enables face detection with a greatly reduced set of examples and outperforms the detection rates of our non iterative detection system based on normalized cross-correlation and affine deformation compensation.","PeriodicalId":322045,"journal":{"name":"2008 15th International Conference on Systems, Signals and Image Processing","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Iterative unsupervised object detection system\",\"authors\":\"S. Onis, Henri Sanson, Christophe Garcia\",\"doi\":\"10.1109/IWSSIP.2008.4604450\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Current object detection systems provide good results, at the expense of requiring a large training database. This paper presents an unsupervised iterative object detection system using a selection of previously detected objects in order to perform new object detection. Our experiments show that this method enables face detection with a greatly reduced set of examples and outperforms the detection rates of our non iterative detection system based on normalized cross-correlation and affine deformation compensation.\",\"PeriodicalId\":322045,\"journal\":{\"name\":\"2008 15th International Conference on Systems, Signals and Image Processing\",\"volume\":\"90 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 15th International Conference on Systems, Signals and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWSSIP.2008.4604450\",\"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 15th International Conference on Systems, Signals and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWSSIP.2008.4604450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Current object detection systems provide good results, at the expense of requiring a large training database. This paper presents an unsupervised iterative object detection system using a selection of previously detected objects in order to perform new object detection. Our experiments show that this method enables face detection with a greatly reduced set of examples and outperforms the detection rates of our non iterative detection system based on normalized cross-correlation and affine deformation compensation.
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