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

Authors:
Dilip K. Prasad;Maylor K. H. Leung;Chai Quek;Siu-Yeung Cho
Affiliations:
School of Computer Engineering, Nanyang Technological University, 639798, Singapore;Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman (Kampar), Malaysia;School of Computer Engineering, Nanyang Technological University, 639798, Singapore;University of Nottingham Ningbo, China
Venue:
Image and Vision Computing
Year:
2012

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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.