Constructing an optimal decision tree for FAST corner point detection

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.

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

Springer Nature

Rough Sets and Knowledge Technology

Conference/Event Name
6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011


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