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dc.contributor.authorPapesca, Michael
dc.date.accessioned2017-05-19T16:14:22Z
dc.date.accessioned2020-06-22T14:32:30Z
dc.date.available2017-05-19T16:14:22Z
dc.date.available2020-06-22T14:32:30Z
dc.date.issued2017-05
dc.identifier.urihttp://hdl.handle.net/20.500.12648/737
dc.description.abstractThe Ant Colony Optimization (ACO) is a popular optimization algorithm that finds use in multiple application areas. Though not among the common uses of this algorithm, edge detection in image analysis is a very functional application of this meta-heuristic. To improve the edge detection capabilities, the inherent parallel nature of the ACO method can be combined with the distributed computing framework provided by the Hadoop/Map-Reduce infrastructure. The latter provides a simple, scalable and fault-tolerant distributed processing paradigm that has been popular in industry and the academic community. In this thesis, we explore the Elastic MapReduce service provided by Amazon Web Services to implement ACO algorithm for edge detection in images, and study its scalability and effectiveness by standard metrics. In addition, we demonstrate a filtering technique to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection.en_US
dc.language.isoen_USen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectResearch Subject Categories::MATHEMATICS::Applied mathematics::Theoretical computer scienceen_US
dc.subjectAnt Colony Optimization (ACO)en_US
dc.subjectMathematical optimizationen_US
dc.subjectImage processingen_US
dc.subjectBig dataen_US
dc.subjectHadoop/Map-Reduceen_US
dc.titleEdge detection using parallel ant colony optimization with Hadoop MapReduce: implementation and scalabilityen_US
dc.typeThesisen_US
refterms.dateFOA2020-06-22T14:32:30Z
dc.description.institutionSUNY College at New Paltz


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Attribution-NonCommercial-NoDerivs 3.0 United States
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