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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), doi:10.1016/j.imavis.2012.06.010

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
Public URL
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