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

Prasad, Dilip K. and Leung, Maylor K.H. and Quek, Chai and Cho, Siu-Yeung (2012) A novel framework for making dominant point detection methods non-parametric. Image and Vision Computing, 30 (11). pp. 843-859. ISSN 0262-8856

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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.

Item Type: Article
Keywords: Non-parametric; Line fitting; Polygonal approximation; Dominant points; Digital curves
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Electrical and Electronic Engineering
Identification Number: 10.1016/j.imavis.2012.06.010
Depositing User: LIN, Zhiren
Date Deposited: 25 Oct 2017 10:36
Last Modified: 25 Oct 2017 10:42
URI: http://eprints.nottingham.ac.uk/id/eprint/47521

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