Application of evolutionary and swarm optimization in computer vision: a literature survey
Nakane, Takumi, Bold, Naranchimeg, Sun, Haitian, Lu, Xuequan, Akashi, Takuya and Zhang, Chao 2020, Application of evolutionary and swarm optimization in computer vision: a literature survey, IPSJ Transactions on Computer Vision and Applications, vol. 12, no. 3, pp. 1-34, doi: 10.1186/s41074-020-00065-9.
Attached Files
Name
Description
MIMEType
Size
Downloads
Title
Application of evolutionary and swarm optimization in computer vision: a literature survey
Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in solving combinatorial and NP-hard optimization problems in various research fields. However, in the field of computer vision, related surveys have not been updated during the last decade. In this study, inspired by the recent development of deep neural networks in computer vision, which embed large-scale optimization problems, we first describe a literature survey conducted to compensate for the lack of relevant research in this area. Specifically, applications related to the genetic algorithm and differential evolution from EAs, as well as particle swarm optimization and ant colony optimization from SAs and their variants, are mainly considered in this survey.
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.