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- Publisher Website: 10.1016/j.imavis.2005.07.001
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Article: A robust iterative hypothesis testing design of the repeated genetic algorithm
Title | A robust iterative hypothesis testing design of the repeated genetic algorithm |
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Authors | |
Issue Date | 2005 |
Citation | Image and Vision Computing, 2005, v. 23 n. 11, p. 972-980 How to Cite? |
Abstract | The genetic algorithm is a simple and interesting optimization method for a wide variety of computer vision problems. However, its performance is often brittle and degrades drastically with increasing input problem complexity. While this problem is difficult to overcome due to the stochastic nature of the algorithm, this paper shows that a robust statistical design using sequential sampling, repeated independent trials and hypothesis testing can be used to greatly alleviate the degradation. The working principle is as follows: The probability of success P of a stochastic algorithm A (in this case A is the genetic algorithm) can be estimated by running N copies of A simultaneously or running A repeatedly N times. Such a scheme is generally referred to as the parallel or repeated (genetic) algorithm. By hypothesis testing, P can be tested with a required figure of merit (i.e. the level of significance). This is used in turn to adjust N in an iterative scheme to maintain a constant P repeated, achieving a robust feedback loop. Experimental results on both synthetic and real images are reported on the application of this novel algorithm to an affine object detection problem and a free form 3D object registration problem. © 2005 Elsevier B.V. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/196664 |
ISSN | 2015 Impact Factor: 1.766 2015 SCImago Journal Rankings: 1.700 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yuen, SY | - |
dc.contributor.author | Lam, HS | - |
dc.contributor.author | Fong, CK | - |
dc.contributor.author | Chen, SF | - |
dc.contributor.author | Chow, CK | - |
dc.date.accessioned | 2014-04-24T02:10:32Z | - |
dc.date.available | 2014-04-24T02:10:32Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Image and Vision Computing, 2005, v. 23 n. 11, p. 972-980 | - |
dc.identifier.issn | 0262-8856 | - |
dc.identifier.uri | http://hdl.handle.net/10722/196664 | - |
dc.description.abstract | The genetic algorithm is a simple and interesting optimization method for a wide variety of computer vision problems. However, its performance is often brittle and degrades drastically with increasing input problem complexity. While this problem is difficult to overcome due to the stochastic nature of the algorithm, this paper shows that a robust statistical design using sequential sampling, repeated independent trials and hypothesis testing can be used to greatly alleviate the degradation. The working principle is as follows: The probability of success P of a stochastic algorithm A (in this case A is the genetic algorithm) can be estimated by running N copies of A simultaneously or running A repeatedly N times. Such a scheme is generally referred to as the parallel or repeated (genetic) algorithm. By hypothesis testing, P can be tested with a required figure of merit (i.e. the level of significance). This is used in turn to adjust N in an iterative scheme to maintain a constant P repeated, achieving a robust feedback loop. Experimental results on both synthetic and real images are reported on the application of this novel algorithm to an affine object detection problem and a free form 3D object registration problem. © 2005 Elsevier B.V. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Image and Vision Computing | - |
dc.title | A robust iterative hypothesis testing design of the repeated genetic algorithm | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.imavis.2005.07.001 | - |
dc.identifier.scopus | eid_2-s2.0-25144484476 | - |
dc.identifier.volume | 23 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 972 | - |
dc.identifier.epage | 980 | - |
dc.identifier.isi | WOS:000232520600004 | - |