Constructing an optimal decision tree for FAST corner point detection

Handle URI:
http://hdl.handle.net/10754/564328
Title:
Constructing an optimal decision tree for FAST corner point detection
Authors:
Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
In 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Publisher:
Springer Science + Business Media
Journal:
Rough Sets and Knowledge Technology
Conference/Event name:
6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011
Issue Date:
2011
DOI:
10.1007/978-3-642-24425-4_26
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783642244247
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlkhalid, Abdulazizen
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-04T06:23:51Zen
dc.date.available2015-08-04T06:23:51Zen
dc.date.issued2011en
dc.identifier.isbn9783642244247en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-24425-4_26en
dc.identifier.urihttp://hdl.handle.net/10754/564328en
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.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectcorner point detectionen
dc.subjectDecision treeen
dc.subjectdynamic programmingen
dc.subjectgreedy algorithmen
dc.titleConstructing an optimal decision tree for FAST corner point detectionen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Groupen
dc.identifier.journalRough Sets and Knowledge Technologyen
dc.conference.date9 October 2011 through 12 October 2011en
dc.conference.name6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011en
dc.conference.locationBanff, ABen
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
kaust.authorAlkhalid, Abdulazizen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.