- 期刊
- OpenAccess
Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions
並列摘要
In this paper, we present the use of two evolutionary algorithms to estimate fundamental matrices. We first propose a modification of the Hybrid Taguchi Genetic Algorithm (HTGA) that employs a single objective function, either geometric or algebraic distance, for optimization. We then propose to use a multi-objective optimization algorithm, Intelligent Multi-Objective Evolutionary Algorithm (IMOEA), to optimize both geometric and algebraic distances concurrently. Our experiments show that the proposed modified HTGA (MHTGA) and IMOEA produce more accurate estimation of fundamental matrices than the traditional Genetic Algorithm (GA) and the original HTGA do.
延伸閱讀
- Huang, J. F., Lai, S. H., & Cheng, C. M. (2007). Robust Fundamental Matrix Estimation with Accurate Outlier Detection. Journal of Information Science and Engineering, 23(4), 1213-1225. https://doi.org/10.6688/JISE.2007.23.4.16
- Jui, P. C. (2015). Efficient Algorithms for Constant Multiplications and Their Hardware Implementation [doctoral dissertation, National Central University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0031-0412201512043075
- Zhang, X., & Xiang, S. W. (2013). Solving Optimization Problems on Hermitian Matrix Functions with Applications. Journal of Applied Mathematics, 2013(), 894-904-493. https://doi.org/10.1155/2013/593549
- Zdunek, R., & Cichocki, A. (2008). Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems. Computational Intelligence and Neuroscience, 2008(), 139-151. https://doi.org/10.1155/2008/939567
- 黃敬庭（2023）。Design of Evolutionary Algorithm for Solving Multimodal Continuous Optimization Problems〔碩士論文，國立臺灣師範大學〕。華藝線上圖書館。https://doi.org/10.6345/NTNU202300305