{"title":"B-spline curve fitting based on dynamic adjustment of knot vector using feature points.","authors":"Xiaobing Chen, Shuxin Guo, Rongrong Wang, Chuangchuang Zhang, Jianchu Lin, Shang Chen","doi":"10.1371/journal.pone.0325458","DOIUrl":null,"url":null,"abstract":"<p><p>An essential challenge in B-spline curve fitting is how to produce a B-spline curve that satisfies the accuracy requirement with a minimal number of knots and control points. This paper suggests a better algorithm based on feature points method. During the curve approximation process, the projection points of data points and their parameters are calculated, and the data point parameters are corrected to achieve dynamic adjustment of the knot vector. At the same time, traditional methods are improved in terms of initial feature point selection and new feature points determination. The experimental results indicate that the B-spline curve produced using the method in this work has higher fitting accuracy, fewer control points, and shorter fitting time.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 6","pages":"e0325458"},"PeriodicalIF":2.6000,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204621/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS ONE","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1371/journal.pone.0325458","RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
An essential challenge in B-spline curve fitting is how to produce a B-spline curve that satisfies the accuracy requirement with a minimal number of knots and control points. This paper suggests a better algorithm based on feature points method. During the curve approximation process, the projection points of data points and their parameters are calculated, and the data point parameters are corrected to achieve dynamic adjustment of the knot vector. At the same time, traditional methods are improved in terms of initial feature point selection and new feature points determination. The experimental results indicate that the B-spline curve produced using the method in this work has higher fitting accuracy, fewer control points, and shorter fitting time.
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