A novel framework for making dominant point detection methods non-parametric
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 framewo...
| Main Authors: | , , , |
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| Format: | Article |
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Elsevier
2012
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| Online Access: | https://eprints.nottingham.ac.uk/47521/ |
| _version_ | 1848797566462853120 |
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| author | Prasad, Dilip K. Leung, Maylor K.H. Quek, Chai Cho, Siu-Yeung |
| author_facet | Prasad, Dilip K. Leung, Maylor K.H. Quek, Chai Cho, Siu-Yeung |
| author_sort | Prasad, Dilip K. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:05:55Z |
| format | Article |
| id | nottingham-47521 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:05:55Z |
| publishDate | 2012 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-475212020-04-29T14:56:59Z https://eprints.nottingham.ac.uk/47521/ A novel framework for making dominant point detection methods non-parametric Prasad, Dilip K. Leung, Maylor K.H. Quek, Chai Cho, Siu-Yeung 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. Elsevier 2012-06-30 Article PeerReviewed 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 Non-parametric; Line fitting; Polygonal approximation; Dominant points; Digital curves http://www.sciencedirect.com/science/article/pii/S0262885612000984# doi:10.1016/j.imavis.2012.06.010 doi:10.1016/j.imavis.2012.06.010 |
| spellingShingle | Non-parametric; Line fitting; Polygonal approximation; Dominant points; Digital curves Prasad, Dilip K. Leung, Maylor K.H. Quek, Chai Cho, Siu-Yeung A novel framework for making dominant point detection methods non-parametric |
| title | A novel framework for making dominant point detection methods non-parametric |
| title_full | A novel framework for making dominant point detection methods non-parametric |
| title_fullStr | A novel framework for making dominant point detection methods non-parametric |
| title_full_unstemmed | A novel framework for making dominant point detection methods non-parametric |
| title_short | A novel framework for making dominant point detection methods non-parametric |
| title_sort | novel framework for making dominant point detection methods non-parametric |
| topic | Non-parametric; Line fitting; Polygonal approximation; Dominant points; Digital curves |
| url | https://eprints.nottingham.ac.uk/47521/ https://eprints.nottingham.ac.uk/47521/ https://eprints.nottingham.ac.uk/47521/ |