{"title":"利用梯度方向改进最小二乘拟合","authors":"T. Petković, S. Lončarić","doi":"10.1109/CRV.2014.38","DOIUrl":null,"url":null,"abstract":"Straight line fitting is an important problem in computer and robot vision. We propose a novel method for least squares line fitting that uses both the point coordinates and the local gradient orientation to fit an optimal line by minimizing the proposed algebraic distance. The proposed inclusion of gradient orientation offers several advantages: (a) one data point is sufficient for the line fit, (b) for the same number of points the fit is more precise due to inclusion of gradient orientation, and (c) outliers can be rejected based on the gradient orientation or the distance to line.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Using Gradient Orientation to Improve Least Squares Line Fitting\",\"authors\":\"T. Petković, S. Lončarić\",\"doi\":\"10.1109/CRV.2014.38\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Straight line fitting is an important problem in computer and robot vision. We propose a novel method for least squares line fitting that uses both the point coordinates and the local gradient orientation to fit an optimal line by minimizing the proposed algebraic distance. The proposed inclusion of gradient orientation offers several advantages: (a) one data point is sufficient for the line fit, (b) for the same number of points the fit is more precise due to inclusion of gradient orientation, and (c) outliers can be rejected based on the gradient orientation or the distance to line.\",\"PeriodicalId\":385422,\"journal\":{\"name\":\"2014 Canadian Conference on Computer and Robot Vision\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Canadian Conference on Computer and Robot Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CRV.2014.38\",\"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 Canadian Conference on Computer and Robot Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRV.2014.38","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Gradient Orientation to Improve Least Squares Line Fitting
Straight line fitting is an important problem in computer and robot vision. We propose a novel method for least squares line fitting that uses both the point coordinates and the local gradient orientation to fit an optimal line by minimizing the proposed algebraic distance. The proposed inclusion of gradient orientation offers several advantages: (a) one data point is sufficient for the line fit, (b) for the same number of points the fit is more precise due to inclusion of gradient orientation, and (c) outliers can be rejected based on the gradient orientation or the distance to line.