Dilip K. Prasad
A novel framework for making dominant point detection methods non-parametric
Prasad, Dilip K.; Leung, Maylor K.H.; Quek, Chai; Cho, Siu-Yeung
Maylor K.H. Leung
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|
|Peer Reviewed||Peer Reviewed|
|Keywords||Non-parametric; Line fitting; Polygonal approximation; Dominant points; Digital curves|
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