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A frame-based probabilistic local verification method for robust correspondence
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AI
DOI：10.1016/j.isprsjprs.2022.08.015.png)
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Abstract
En 中文
Establishing reliable feature correspondence between two sets of features is a fundamental task in image processing. In this paper, we propose a novel probabilistic local verification method to reject false feature matches. We exploit the local affine frame to calculate the re-projection error, and develop a novel probabilistic model to estimate the correspondence confidence according to the error. The correspondence confidence is evaluated by calculating the posterior probability based on a two-layer mixture model. The key parameters of the proposed method can be adaptively estimated by alternatively maximizing and updating a second lower bound function. We also suggest that the adjacent inlier neighbors are good neighbors and thereby proposing a confidence-distance-ratio strategy to balance the inlier confidence and spatial distance. Our method mostly outperforms other state-of-the-art methods by over ten percentage points in the success rate of UAV localization tasks, and by over six percentage points in the F-measure on multiple public test datasets.
Keywords:
Locality Preservation Matching
Robust feature correspondence
Outlier rejection
Mismatch removal
UAV localization
Image-based localization
Feature matching
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