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Title: Optimizing maximally stable extremal regions (MSER) parameters using the particle swarm optimization algorithm
Authors: Davis, Jeremy E.
Bednar, Amy E.
Goodin, Christopher T.
Keywords: Computer algorithms
Mathematical optimization
Swarm intelligence
Problem solving
Image analysis
Publisher: Information Technology Laboratory (U.S.)
Geotechnical and Structures Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC TR-19-25
Abstract: The particle swarm optimization algorithm is a common method for finding solutions to problems that would otherwise require a brute-force search. The maximally stable extremal region algorithm is a computer vision technique that can be used for object or region detection in images. This paper explores the use of the particle swarm optimization algorithm to find an acceptable set of parameters for the maximally stable extremal region algorithm. Additions to the basic particle swarm optimization algorithm that allows finding a set of acceptable parameters from an infinite combination of parameters that 1) do not violate bounds implicitly enforced by the maximally stable extremal region algorithm, and 2) generate acceptable training and testing results are described. The output of the optimized maximally stable extremal region algorithm will be used in future work to segment potential regions of interest for image classification.
Description: Technical Report
Gov't Doc #: ERDC TR-19-25
Rights: Approved for Public Release; Distribution is Unlimited
Size: 39 pages / 2.507 Mb
Types of Materials: PDF
Appears in Collections:Technical Report

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