{"title":"利用遗传算法提高组合视觉系统的图像组合性能","authors":"S. Elesina, Olga Lomteva","doi":"10.1109/MECO.2014.6862682","DOIUrl":null,"url":null,"abstract":"Research of genetic algorithm with the aim to receive optimum settings while it being used in combined vision systems has been carried out. To decrease the complexity in global extremum search advanced angles of virtual terrain model are supposed to be used. System performance is shown to be increased 5 times using such approach.","PeriodicalId":416168,"journal":{"name":"2014 3rd Mediterranean Conference on Embedded Computing (MECO)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Increase of image combination performance in combined vision systems using genetic algorithm\",\"authors\":\"S. Elesina, Olga Lomteva\",\"doi\":\"10.1109/MECO.2014.6862682\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Research of genetic algorithm with the aim to receive optimum settings while it being used in combined vision systems has been carried out. To decrease the complexity in global extremum search advanced angles of virtual terrain model are supposed to be used. System performance is shown to be increased 5 times using such approach.\",\"PeriodicalId\":416168,\"journal\":{\"name\":\"2014 3rd Mediterranean Conference on Embedded Computing (MECO)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 3rd Mediterranean Conference on Embedded Computing (MECO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MECO.2014.6862682\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 3rd Mediterranean Conference on Embedded Computing (MECO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MECO.2014.6862682","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Increase of image combination performance in combined vision systems using genetic algorithm
Research of genetic algorithm with the aim to receive optimum settings while it being used in combined vision systems has been carried out. To decrease the complexity in global extremum search advanced angles of virtual terrain model are supposed to be used. System performance is shown to be increased 5 times using such approach.