@article { , title = {A novel framework for making dominant point detection methods non-parametric}, 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.}, doi = {10.1016/j.imavis.2012.06.010}, eissn = {0262-8856}, issn = {0262-8856}, issue = {11}, journal = {Image and Vision Computing}, note = { School:C-Eng7,}, publicationstatus = {Published}, publisher = {Elsevier}, url = {https://nottingham-repository.worktribe.com/output/710414}, volume = {30}, keyword = {Non-parametric, Line fitting, Polygonal approximation, Dominant points, Digital curves}, author = {Prasad, Dilip K. and Leung, Maylor K.H. and Quek, Chai and Cho, Siu-Yeung} }