An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting
An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting
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Basel: MDPI AG
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Language
English
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Publisher
Basel: MDPI AG
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Contents
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 op...
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Full title
An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting
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TN_cdi_proquest_journals_3090896649
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3090896649
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ISSN
2079-9292
E-ISSN
2079-9292
DOI
10.3390/electronics13152972
