A parameter independent line fitting method

Dilip K. Prasad, Chai Quek, Maylor K.H. Leung, Siu Yeung Cho

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

24 Citations (Scopus)

Abstract

We prove that when a line is approximated using digital line, the error in the slope of the digital line has a definite upper bound and is strongly dependent on the two pixels chosen for defining the digital line. Thus, an analytical expression of the maximum deviation of the pixels from the digital line can be derived. Using this, the conventional line fitting methods that use maximum tolerable deviation as the optimization goal can be made control-parameter independent. This error bound can be used to make the most recent and sophisticated line fitting methods parameter independent and more robust to digitization noises. In our knowledge, this is the first line fitting method completely devoid of any control parameter. Such control-parameter independent line fitting algorithm retains the characteristics of the digital curve with sufficient reliability and precision and provides good dimensionality reduction in representing the digital curves. Extensive results have been generated for 9 datasets comprising of about a hundred thousand images. The proposed method shows robust and repeatable performance across all the datasets with low standard deviation in the performance.

Original languageEnglish
Title of host publication1st Asian Conference on Pattern Recognition, ACPR 2011
PublisherIEEE Computer Society
Pages441-445
Number of pages5
ISBN (Print)9781457701221
DOIs
Publication statusPublished - 2011
Event1st Asian Conference on Pattern Recognition, ACPR 2011 - Beijing, China
Duration: 28 Nov 201128 Nov 2011

Publication series

Name1st Asian Conference on Pattern Recognition, ACPR 2011

Conference

Conference1st Asian Conference on Pattern Recognition, ACPR 2011
Country/TerritoryChina
CityBeijing
Period28/11/1128/11/11

Keywords

  • digitization
  • dominant points
  • line fitting

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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