کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
526777 | 869225 | 2012 | 17 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A novel framework for making dominant point detection methods non-parametric A novel framework for making dominant point detection methods non-parametric](/preview/png/526777.png)
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.
► Bound of the max. deviation of pixels from a digitized line segment is derived.
► The bound is used as a natural benchmark for dominant point detection (DPD) methods.
► DPD methods can be made parameter-free and non-heuristic using it.
► Three different DPD methods have been made parameter independent.
Journal: Image and Vision Computing - Volume 30, Issue 11, November 2012, Pages 843–859