A novel framework for making dominant point detection methods non-parametricTools Prasad, Dilip K., Leung, Maylor K.H., 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 Full text not available from this repository.AbstractMost 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.
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