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Open AccessArticle
An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting
by
Gundu Mohamed Hassan
Gundu Mohamed Hassan 1,†,
Zijian Min
Zijian Min 1,†,
Vijay Kakani
Vijay Kakani 2,* and
Geun-Sik Jo
Geun-Sik Jo 1,*
1
Artificial Intelligence Laboratory, Electrical and Computer Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea
2
Integrated System Engineering, Inha University, 100 Inharo, Michuhol-gu, Incheon 22212, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2024, 13(15), 2972; https://doi.org/10.3390/electronics13152972 (registering DOI)
Submission received: 21 June 2024
/
Revised: 23 July 2024
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Accepted: 23 July 2024
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Published: 27 July 2024
Abstract
Robust geometric fitting is one of the crucial and fundamental problems in computer vision and pattern recognition. While random sampling and consensus maximization have been popular strategies for robust fitting, finding a balance between optimization quality and computational efficiency remains a persistent obstacle. In this paper, we adopt an optimization perspective and introduce a novel maximum consensus robust fitting algorithm that incorporates the maximum entropy framework into the consensus maximization problem. Specifically, we incorporate the probability distribution of inliers calculated using maximum entropy with consensus constraints. Furthermore, we introduce an improved relaxed and accelerated alternating direction method of multipliers (R-A-ADMMs) strategy tailored to our framework, facilitating an efficient solution to the optimization problem. Our proposed algorithm demonstrates superior performance compared to state-of-the-art methods on both synthetic and contaminated real datasets, particularly when dealing with contaminated datasets containing a high proportion of outliers.
Share and Cite
MDPI and ACS Style
Hassan, G.M.; Min, Z.; Kakani, V.; Jo, G.-S.
An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting. Electronics 2024, 13, 2972.
https://doi.org/10.3390/electronics13152972
AMA Style
Hassan GM, Min Z, Kakani V, Jo G-S.
An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting. Electronics. 2024; 13(15):2972.
https://doi.org/10.3390/electronics13152972
Chicago/Turabian Style
Hassan, Gundu Mohamed, Zijian Min, Vijay Kakani, and Geun-Sik Jo.
2024. "An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting" Electronics 13, no. 15: 2972.
https://doi.org/10.3390/electronics13152972
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