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Object recognition by genetic algorithm
| Title: | Object recognition by genetic algorithm. |
 126 views 32 downloads |
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| Name(s): |
Li, Jianhua. Florida Atlantic University, Degree grantor Han, Chingping (Jim), Thesis advisor Zhuang, Hanqi, Thesis advisor College of Engineering and Computer Science Department of Ocean and Mechanical Engineering |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Issuance: | monographic | |
| Date Issued: | 1995 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 124 p. | |
| Language(s): | English | |
| Summary: | Vision systems have been widely used for parts inspection in electronics assembly lines. In order to improve the overall performance of a visual inspection system, it is important to employ an efficient object recognition algorithm. In this thesis work, a genetic algorithm based correlation algorithm is designed for the task of visual electronic parts inspection. The proposed procedure is composed of two stages. In the first stage, a genetic algorithm is devised to find a sufficient number of candidate image windows. For each candidate window, the correlation is performed between the sampled template and the image pattern inside the window. In the second stage, local searches are conducted in the neighborhood of these candidate windows. Among all the searched locations, the one that has a highest correlation value with the given template is selected as the best matched location. To apply the genetic algorithm technique, a number of important issues, such as selection of a fitness function, design of a coding scheme, and tuning of genetic parameters are addressed in the thesis. Experimental studies have confirmed that the proposed GA-based correlation method is much more effective in terms of accuracy and speed in locating the desired object, compared with the existing Monte-Carlo random search method. | |
| Identifier: | 15225 (digitool), FADT15225 (IID), fau:11997 (fedora) | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): |
College of Engineering and Computer Science Thesis (M.S.)--Florida Atlantic University, 1995. |
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| Subject(s): |
Genetic algorithms Robots--Control systems Computer vision Quality control--Optical methods |
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| Held by: | Florida Atlantic University Libraries | |
| Persistent Link to This Record: | http://purl.flvc.org/fcla/dt/15225 | |
| Sublocation: | Digital Library | |
| Use and Reproduction: | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU | |
| Is Part of Series: | Florida Atlantic University Digital Library Collections. |
