{"title":"基于遗传搜索的纹理识别特征检测器的生成","authors":"J. Bala, K. A. Jong","doi":"10.1109/TAI.1990.130443","DOIUrl":null,"url":null,"abstract":"A genetic-algorithm-based methodology for learning a set of feature detectors for texture discrimination is presented. This methodology is incorporated into a vision system that learns to classify noisy examples of different texture classes by evolving populations of simple and texture-specific local spatial feature detectors. The results of preliminary work confirm the utility of the genetic algorithm in solving problems within the specified domain.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Generation of feature detectors for texture discrimination by genetic search\",\"authors\":\"J. Bala, K. A. Jong\",\"doi\":\"10.1109/TAI.1990.130443\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A genetic-algorithm-based methodology for learning a set of feature detectors for texture discrimination is presented. This methodology is incorporated into a vision system that learns to classify noisy examples of different texture classes by evolving populations of simple and texture-specific local spatial feature detectors. The results of preliminary work confirm the utility of the genetic algorithm in solving problems within the specified domain.<<ETX>>\",\"PeriodicalId\":366276,\"journal\":{\"name\":\"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1990-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1990.130443\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1990.130443","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generation of feature detectors for texture discrimination by genetic search
A genetic-algorithm-based methodology for learning a set of feature detectors for texture discrimination is presented. This methodology is incorporated into a vision system that learns to classify noisy examples of different texture classes by evolving populations of simple and texture-specific local spatial feature detectors. The results of preliminary work confirm the utility of the genetic algorithm in solving problems within the specified domain.<>