基于最大熵的线性车道识别与跟踪边缘提取方法研究

IEEE Proceedings. Intelligent Vehicles Symposium, 2005. Pub Date : 2005-06-06 DOI:10.1109/IVS.2005.1505211

Wang Rong-ben, Yu Tian-hong, Jin Li-sheng, Chu Jiang-wei, Gu Bai-yuan

{"title":"基于最大熵的线性车道识别与跟踪边缘提取方法研究","authors":"Wang Rong-ben, Yu Tian-hong, Jin Li-sheng, Chu Jiang-wei, Gu Bai-yuan","doi":"10.1109/IVS.2005.1505211","DOIUrl":null,"url":null,"abstract":"In order to better abstract lane mark edge and identify it, this paper proposes a new edge extraction method based on maximum entropy. This method combines both one-dimension and two-dimension entropy information. Meanwhile, image window variation technology is also applied for lane mark edge extraction and lane mark parameters can be acquired based on the bi-normalized adjustable template. Finally lane mark real-time tracking is realized by applying trapezia AOI method.","PeriodicalId":386189,"journal":{"name":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Edge extraction method study based on maximum entropy for linear lane identifying and tracking\",\"authors\":\"Wang Rong-ben, Yu Tian-hong, Jin Li-sheng, Chu Jiang-wei, Gu Bai-yuan\",\"doi\":\"10.1109/IVS.2005.1505211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to better abstract lane mark edge and identify it, this paper proposes a new edge extraction method based on maximum entropy. This method combines both one-dimension and two-dimension entropy information. Meanwhile, image window variation technology is also applied for lane mark edge extraction and lane mark parameters can be acquired based on the bi-normalized adjustable template. Finally lane mark real-time tracking is realized by applying trapezia AOI method.\",\"PeriodicalId\":386189,\"journal\":{\"name\":\"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2005.1505211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2005.1505211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

为了更好地提取车道标记边缘并进行识别，提出了一种基于最大熵的车道标记边缘提取方法。该方法结合了一维和二维熵信息。同时，将图像窗口变分技术应用于车道标记边缘提取，基于双归一化可调模板获取车道标记参数。最后利用梯形AOI方法实现车道标记的实时跟踪。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Edge extraction method study based on maximum entropy for linear lane identifying and tracking

In order to better abstract lane mark edge and identify it, this paper proposes a new edge extraction method based on maximum entropy. This method combines both one-dimension and two-dimension entropy information. Meanwhile, image window variation technology is also applied for lane mark edge extraction and lane mark parameters can be acquired based on the bi-normalized adjustable template. Finally lane mark real-time tracking is realized by applying trapezia AOI method.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Proceedings. Intelligent Vehicles Symposium, 2005.

自引率

0.00%

发文量