{"title":"基于遗传规划的避障视觉特征检测器学习","authors":"Andrew J. Marek, W. Smart, Martin C. Martin","doi":"10.1109/CVPRW.2003.10066","DOIUrl":null,"url":null,"abstract":"In this paper, we describe the use of Genetic Programming (GP) techniques to learn a visual feature detection for a mobile robot navigation task. We provide experimental results across a number of different environments, each with different characteristics, and draw conclusions about the performance of the learned feature detector. We also explore the utility of seeding the initial population with a previously evolved individual, and discuss the performance of the resulting individuals.","PeriodicalId":121249,"journal":{"name":"2003 Conference on Computer Vision and Pattern Recognition Workshop","volume":"690 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Learning Visual Feature Detectors for Obstacle Avoidance using Genetic Programming\",\"authors\":\"Andrew J. Marek, W. Smart, Martin C. Martin\",\"doi\":\"10.1109/CVPRW.2003.10066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we describe the use of Genetic Programming (GP) techniques to learn a visual feature detection for a mobile robot navigation task. We provide experimental results across a number of different environments, each with different characteristics, and draw conclusions about the performance of the learned feature detector. We also explore the utility of seeding the initial population with a previously evolved individual, and discuss the performance of the resulting individuals.\",\"PeriodicalId\":121249,\"journal\":{\"name\":\"2003 Conference on Computer Vision and Pattern Recognition Workshop\",\"volume\":\"690 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2003 Conference on Computer Vision and Pattern Recognition Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPRW.2003.10066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2003 Conference on Computer Vision and Pattern Recognition Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2003.10066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning Visual Feature Detectors for Obstacle Avoidance using Genetic Programming
In this paper, we describe the use of Genetic Programming (GP) techniques to learn a visual feature detection for a mobile robot navigation task. We provide experimental results across a number of different environments, each with different characteristics, and draw conclusions about the performance of the learned feature detector. We also explore the utility of seeding the initial population with a previously evolved individual, and discuss the performance of the resulting individuals.
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