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dc.contributor.authorAlkhalid, Abdulaziz
dc.contributor.authorChikalov, Igor
dc.contributor.authorMoshkov, Mikhail
dc.date.accessioned2015-08-04T06:23:51Z
dc.date.available2015-08-04T06:23:51Z
dc.date.issued2011
dc.identifier.isbn9783642244247
dc.identifier.issn03029743
dc.identifier.doi10.1007/978-3-642-24425-4_26
dc.identifier.urihttp://hdl.handle.net/10754/564328
dc.description.abstractIn this paper, we consider a problem that is originated in computer vision: determining an optimal testing strategy for the corner point detection problem that is a part of FAST algorithm [11,12]. The problem can be formulated as building a decision tree with the minimum average depth for a decision table with all discrete attributes. We experimentally compare performance of an exact algorithm based on dynamic programming and several greedy algorithms that differ in the attribute selection criterion. © 2011 Springer-Verlag.
dc.publisherSpringer Nature
dc.subjectcorner point detection
dc.subjectDecision tree
dc.subjectdynamic programming
dc.subjectgreedy algorithm
dc.titleConstructing an optimal decision tree for FAST corner point detection
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
dc.identifier.journalRough Sets and Knowledge Technology
dc.conference.date9 October 2011 through 12 October 2011
dc.conference.name6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011
dc.conference.locationBanff, AB
kaust.personChikalov, Igor
kaust.personMoshkov, Mikhail
kaust.personAlkhalid, Abdulaziz


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