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    Constructing an optimal decision tree for FAST corner point detection

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    Type
    Conference Paper
    Authors
    Alkhalid, Abdulaziz
    Chikalov, Igor
    Moshkov, Mikhail cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
    Date
    2011
    Permanent link to this record
    http://hdl.handle.net/10754/564328
    
    Metadata
    Show full item record
    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.
    Citation
    Alkhalid, A., Chikalov, I., & Moshkov, M. (2011). Constructing an Optimal Decision Tree for FAST Corner Point Detection. Lecture Notes in Computer Science, 187–194. doi:10.1007/978-3-642-24425-4_26
    Publisher
    Springer Nature
    Journal
    Rough Sets and Knowledge Technology
    Conference/Event name
    6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011
    ISBN
    9783642244247
    DOI
    10.1007/978-3-642-24425-4_26
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-24425-4_26
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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