Constructing an optimal decision tree for FAST corner point detection

Abdulaziz Alkhalid, Igor Chikalov, Mikhail Moshkov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationRough Sets and Knowledge Technology
PublisherSpringer Nature
Number of pages8
ISBN (Print)9783642244247
StatePublished - 2011

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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