Constructing an optimal decision tree for FAST corner point detection
Type
Conference PaperKAUST Department
Applied Mathematics and Computational Science ProgramComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Date
2011Permanent link to this record
http://hdl.handle.net/10754/564328
Metadata
Show full item recordAbstract
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_26Publisher
Springer NatureConference/Event name
6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011ISBN
9783642244247ae974a485f413a2113503eed53cd6c53
10.1007/978-3-642-24425-4_26