Spectral-spatial outlier filter for image matching

Junfu Zhou; Ting-Bing Xu; Zhenzhong Wei

doi:10.1117/12.2616326

8 July 2022 Spectral-spatial outlier filter for image matching

Junfu Zhou, Ting-Bing Xu, Zhenzhong Wei

Proceedings Volume 12282, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems; 122820B (2022) https://doi.org/10.1117/12.2616326
Event: 2021 International Conference on Optical Instruments and Technology, 2022, Online Only

Abstract

Feature-based image matching is often contaminated by some mismatches due to the limited representation of descriptors. Existing two-stage mismatch removal filters usually select some seed points first and then remove outliers according to the consistency in neighborhoods. However, the filter's performance is directly influenced by the selection of effective seed points. In this paper, we design an elegant Spectral-spatial Outlier Filter (SOF) to harvest high-accuracy image matching. Specifically, we first calculate eigenvectors of Laplacian matrix from the joint image graph as feature descriptors in the spectral domain to select more reasonable seed points, and then these points are fed into the local a fine verification in the spatial domain in the second stage to effectively remove outliers. Experimental results on challenging datasets demonstrate that the proposed filter further improves the precision of image matching, and steadily outperforms other state-of-the-art methods.

Citation Download Citation

Junfu Zhou, Ting-Bing Xu, and Zhenzhong Wei "Spectral-spatial outlier filter for image matching", Proc. SPIE 12282, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 122820B (8 July 2022); https://doi.org/10.1117/12.2616326

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