Extended randomized hough transform for 2-D arbitrary shape recognition
The extraction of arbitrary 2-D shapes according to specific templates is a very important operation for object recognition in digital image processing and computer vision fields. Because of its robustness to noises and discontinuity of feature points, Generalized Hough Transform (GHT) is a classi...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2008
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| Online Access: | http://hdl.handle.net/10356/13205 |
| _version_ | 1826112737780957184 |
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| author | Lin, Yuan. |
| author2 | Chutatape, Opas |
| author_facet | Chutatape, Opas Lin, Yuan. |
| author_sort | Lin, Yuan. |
| collection | NTU |
| description | The extraction of arbitrary 2-D shapes according to specific templates is a very important operation for object recognition in digital image processing and computer vision fields.
Because of its robustness to noises and discontinuity of feature points, Generalized Hough
Transform (GHT) is a classical and effective technique to tackle this problem. However, as an
extension of the Standard Hough Transform, its computational complexity and memory
requirement are considerably large especially when there is no prior knowledge for the
orientation and scale of the scene object.
The main purpose of this thesis is to develop an alternative algorithm for GHT, to overcome
its shortcomings while maintaining its significant advantage of robustness. Based on the idea
of Randomized Hough Transform, which is a typical probabilistic Hough Transform, four
new algorithms are developed in tins thesis to deal with different cases of object recognition.
As extensions to the basic Randomized Hough Transform from analytic curve detection to
arbitrary shape detection, the random sampling mechanism, convergent mapping mechanism
and dynamic list structured parameter accumulator are used in these proposed algorithms.
Compared with the GHT and Template Matching approaches, their computational complexity
and memory requirement are reduced while their robustness is retained. |
| first_indexed | 2024-10-01T03:11:42Z |
| format | Thesis |
| id | ntu-10356/13205 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:11:42Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/132052023-07-04T15:57:07Z Extended randomized hough transform for 2-D arbitrary shape recognition Lin, Yuan. Chutatape, Opas School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing The extraction of arbitrary 2-D shapes according to specific templates is a very important operation for object recognition in digital image processing and computer vision fields. Because of its robustness to noises and discontinuity of feature points, Generalized Hough Transform (GHT) is a classical and effective technique to tackle this problem. However, as an extension of the Standard Hough Transform, its computational complexity and memory requirement are considerably large especially when there is no prior knowledge for the orientation and scale of the scene object. The main purpose of this thesis is to develop an alternative algorithm for GHT, to overcome its shortcomings while maintaining its significant advantage of robustness. Based on the idea of Randomized Hough Transform, which is a typical probabilistic Hough Transform, four new algorithms are developed in tins thesis to deal with different cases of object recognition. As extensions to the basic Randomized Hough Transform from analytic curve detection to arbitrary shape detection, the random sampling mechanism, convergent mapping mechanism and dynamic list structured parameter accumulator are used in these proposed algorithms. Compared with the GHT and Template Matching approaches, their computational complexity and memory requirement are reduced while their robustness is retained. Master of Engineering 2008-07-30T00:59:47Z 2008-10-20T07:18:56Z 2008-07-30T00:59:47Z 2008-10-20T07:18:56Z 1999 1999 Thesis http://hdl.handle.net/10356/13205 en 170 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Lin, Yuan. Extended randomized hough transform for 2-D arbitrary shape recognition |
| title | Extended randomized hough transform for 2-D arbitrary shape recognition |
| title_full | Extended randomized hough transform for 2-D arbitrary shape recognition |
| title_fullStr | Extended randomized hough transform for 2-D arbitrary shape recognition |
| title_full_unstemmed | Extended randomized hough transform for 2-D arbitrary shape recognition |
| title_short | Extended randomized hough transform for 2-D arbitrary shape recognition |
| title_sort | extended randomized hough transform for 2 d arbitrary shape recognition |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | http://hdl.handle.net/10356/13205 |
| work_keys_str_mv | AT linyuan extendedrandomizedhoughtransformfor2darbitraryshaperecognition |