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

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

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.

Citation

Prasad, D. K., Leung, M. K., Quek, C., & Cho, S.-Y. (in press). A novel framework for making dominant point detection methods non-parametric. Image and Vision Computing, 30(11), https://doi.org/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: 1872-8138
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 30
• Issue: 11
• DOI: https://doi.org/10.1016/j.imavis.2012.06.010
• Keywords: Non-parametric; Line fitting; Polygonal approximation; Dominant points; Digital curves
• Public URL: https://nottingham-repository.worktribe.com/output/710414
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0262885612000984#
• Contract Date: Oct 25, 2017