{"title":"交互式视觉模式识别","authors":"G. Nagy, Jie Zou","doi":"10.1109/ICPR.2002.1048342","DOIUrl":null,"url":null,"abstract":"Computer Assisted Visual Interactive Recognition (CAVIAR) draws on sequential pattern recognition, image database, expert systems, pen computing, and digital camera technology. It is designed to recognize wildflowers and other families of similar objects more accurately than machine vision and faster than most laypersons. The novelty of the approach is that human perceptual ability is exploited through interaction with the image of the unknown object. The computer remembers the characteristics of all previously seen classes, suggests possible operator actions, and displays confidence scores based on already detected features. In one application, consisting of 80 test images of wildflowers, 10 laypersons averaged 80% recognition accuracy at 12 seconds per flower.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Interactive visual pattern recognition\",\"authors\":\"G. Nagy, Jie Zou\",\"doi\":\"10.1109/ICPR.2002.1048342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Computer Assisted Visual Interactive Recognition (CAVIAR) draws on sequential pattern recognition, image database, expert systems, pen computing, and digital camera technology. It is designed to recognize wildflowers and other families of similar objects more accurately than machine vision and faster than most laypersons. The novelty of the approach is that human perceptual ability is exploited through interaction with the image of the unknown object. The computer remembers the characteristics of all previously seen classes, suggests possible operator actions, and displays confidence scores based on already detected features. In one application, consisting of 80 test images of wildflowers, 10 laypersons averaged 80% recognition accuracy at 12 seconds per flower.\",\"PeriodicalId\":159502,\"journal\":{\"name\":\"Object recognition supported by user interaction for service robots\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Object recognition supported by user interaction for service robots\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2002.1048342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Object recognition supported by user interaction for service robots","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2002.1048342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computer Assisted Visual Interactive Recognition (CAVIAR) draws on sequential pattern recognition, image database, expert systems, pen computing, and digital camera technology. It is designed to recognize wildflowers and other families of similar objects more accurately than machine vision and faster than most laypersons. The novelty of the approach is that human perceptual ability is exploited through interaction with the image of the unknown object. The computer remembers the characteristics of all previously seen classes, suggests possible operator actions, and displays confidence scores based on already detected features. In one application, consisting of 80 test images of wildflowers, 10 laypersons averaged 80% recognition accuracy at 12 seconds per flower.