Scopus: 2 cites, Google Scholar: cites
An ant colony based model to optimize parameters in industrial vision
Benchikhi, Loubna (Cadi Ayyad University (Marràqueix, Marroc). Department of Computer Science)
Sadgal, Mohamed (Cadi Ayyad University (Marràqueix, Marroc). Department of Computer Science)
Elfazziki, Aziz (Cadi Ayyad University (Marràqueix, Marroc). Department of Computer Science)
Mansouri, Fatimaezzahra (Cadi Ayyad University (Marràqueix, Marroc). Department of Computer Science)

Data: 2017
Resum: Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models. . . ). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: Image processing ; Industrial vision ; Ant colony optimization ; Quality control
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 16 Núm. 1 (2017) , p. 33-53 (Regular Issue) , ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.957

21 p, 2.1 MB

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