TY - GEN
T1 - Constructing an optimal decision tree for FAST corner point detection
AU - Alkhalid, Abdulaziz
AU - Chikalov, Igor
AU - Moshkov, Mikhail
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/564328
UR - http://link.springer.com/10.1007/978-3-642-24425-4_26
UR - http://www.scopus.com/inward/record.url?scp=80054057922&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24425-4_26
DO - 10.1007/978-3-642-24425-4_26
M3 - Conference contribution
SN - 9783642244247
SP - 187
EP - 194
BT - Rough Sets and Knowledge Technology
PB - Springer Nature
ER -