Constructing an optimal decision tree for FAST corner point detection
KAUST DepartmentComputer, 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
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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.
PublisherSpringer Science + Business Media
Conference/Event name6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011