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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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/100988 http://hdl.handle.net/10220/16700 |
| _version_ | 1824455731174178816 |
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| author | Leung, Maylor Karhang Quek, Chai Cho, Siu-Yeung Prasad, Dilip K. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Leung, Maylor Karhang Quek, Chai Cho, Siu-Yeung Prasad, Dilip K. |
| author_sort | Leung, Maylor Karhang |
| collection | NTU |
| 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 | 2025-02-19T03:42:52Z |
| format | Journal Article |
| id | ntu-10356/100988 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:42:52Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1009882020-05-28T07:18:20Z A novel framework for making dominant point detection methods non-parametric Leung, Maylor Karhang Quek, Chai Cho, Siu-Yeung Prasad, Dilip K. School of Computer Engineering DRNTU::Engineering::Computer science and engineering 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. 2013-10-23T05:18:55Z 2019-12-06T20:31:46Z 2013-10-23T05:18:55Z 2019-12-06T20:31:46Z 2012 2012 Journal Article Prasad, D. K., Leung, M. K., Quek, C., & Cho, S.-Y. (2012). A novel framework for making dominant point detection methods non-parametric. Image and vision computing, 30(11), 843-859. 0262-8856 https://hdl.handle.net/10356/100988 http://hdl.handle.net/10220/16700 10.1016/j.imavis.2012.06.010 en Image and Vision Computing © 2012 Elsevier B.V. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Leung, Maylor Karhang Quek, Chai Cho, Siu-Yeung Prasad, Dilip K. 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 | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/100988 http://hdl.handle.net/10220/16700 |
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