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- W4200503995 abstract "In this paper, we propose a fast and robust feature correspondence verification method based on local consensus. We encode the locality invariance into a continuous probability density function and formulate the problem to be a two posteriori estimation of Bayesian models without accurate transformation estimation. The model can be used without iteration for super-efficiency, and iteratively for SOTA performance. The non-iterative version can directly output the correctness confidence of each match with linearithmic time and linear space complexities. The iterative version requires only 5-10 iterations to update parameters, which can produce SOTA precision or recall. Besides, our model provides remarkable geometry robustness, and is robust to the quality of neighbors even when the simplest spatial knn is used. Numerous experimental results demonstrate the superiority of our method." @default.
- W4200503995 created "2021-12-31" @default.
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- W4200503995 date "2021-11-01" @default.
- W4200503995 modified "2023-10-01" @default.
- W4200503995 title "Fast and Adaptive Local Consensus Verification for Robust Feature Correspondence" @default.
- W4200503995 doi "https://doi.org/10.1109/ccisp52774.2021.9639274" @default.
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