A novel framework for making dominant point detection methods non-parametric

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

Research output: Journal PublicationArticlepeer-review

61 Citations (Scopus)

Abstract

Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves.

Original languageEnglish
Pages (from-to)843-859
Number of pages17
JournalImage and Vision Computing
Volume30
Issue number11
DOIs
Publication statusPublished - Nov 2012

Keywords

  • Digital curves
  • Dominant points
  • Line fitting
  • Non-parametric
  • Polygonal approximation

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

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