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Conference Paper: Minimizing the probabilistic magnitude of active vision errors using genetic algorithm

TitleMinimizing the probabilistic magnitude of active vision errors using genetic algorithm
Authors
KeywordsComputers
Cybernetics
Issue Date1997
PublisherIEEE.
Citation
Computational Cybernetics and Simulation, IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, Orlando, Florida, USA, 12-15 October 1997, v. 3, p. 2713-2718 How to Cite?
AbstractSpatial quantization errors are resulted in digitization. The errors are serious when the size of the pixel is significant compared to the allowable tolerance in the object dimension on the image. In placing the active sensor to perform inspection, displacement of the sensors in orientation and location is common. The difference between observed dimensions obtained by the displaced sensor and the actual dimensions is defined as displacement errors. The density functions of quantization errors and displacement errors depend on the camera resolution and camera locations and orientations. We use genetic algorithm to minimize the probabilistic magnitude of the errors subject to the sensor constraints, such as the resolution, field-of-view, focus, and visibility constraints. Since the objective functions and the constraint functions are both complicated and nonlinear, traditional nonlinear programming may not be efficient and trapping at a local minimum may occur. Using crossover operations, mutation operations, and the stochastic selection in genetic algorithm, trapping can be avoided.
Persistent Identifierhttp://hdl.handle.net/10722/45591
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYang, CCCen_HK
dc.contributor.authorCiarallo, FWen_HK
dc.date.accessioned2007-10-30T06:29:51Z-
dc.date.available2007-10-30T06:29:51Z-
dc.date.issued1997en_HK
dc.identifier.citationComputational Cybernetics and Simulation, IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, Orlando, Florida, USA, 12-15 October 1997, v. 3, p. 2713-2718en_HK
dc.identifier.issn1062-922Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/45591-
dc.description.abstractSpatial quantization errors are resulted in digitization. The errors are serious when the size of the pixel is significant compared to the allowable tolerance in the object dimension on the image. In placing the active sensor to perform inspection, displacement of the sensors in orientation and location is common. The difference between observed dimensions obtained by the displaced sensor and the actual dimensions is defined as displacement errors. The density functions of quantization errors and displacement errors depend on the camera resolution and camera locations and orientations. We use genetic algorithm to minimize the probabilistic magnitude of the errors subject to the sensor constraints, such as the resolution, field-of-view, focus, and visibility constraints. Since the objective functions and the constraint functions are both complicated and nonlinear, traditional nonlinear programming may not be efficient and trapping at a local minimum may occur. Using crossover operations, mutation operations, and the stochastic selection in genetic algorithm, trapping can be avoided.en_HK
dc.format.extent512833 bytes-
dc.format.extent3082 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectComputersen_HK
dc.subjectCyberneticsen_HK
dc.titleMinimizing the probabilistic magnitude of active vision errors using genetic algorithmen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1062-922X&volume=3&spage=2713&epage=2718&date=1997&atitle=Minimizing+the+probabilistic+magnitude+of+active+vision+errors+using+genetic+algorithmen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICSMC.1997.635348en_HK
dc.identifier.hkuros31121-

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