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

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


Dilip K. Prasad

Maylor K.H. Leung

Chai Quek

Siu-Yeung Cho


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.


Prasad, D. K., Leung, M. K., Quek, C., & Cho, S. (in press). A novel framework for making dominant point detection methods non-parametric. Image and Vision Computing, 30(11),

Journal Article Type Article
Acceptance Date Jun 23, 2012
Online Publication Date Jun 30, 2012
Deposit Date Oct 25, 2017
Journal Image and Vision Computing
Print ISSN 0262-8856
Electronic ISSN 0262-8856
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 30
Issue 11
Keywords Non-parametric; Line fitting; Polygonal approximation; Dominant points; Digital curves
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