Abstract
This paper describes the computation of feature point correspondences using the spectra of a Hermitian property matrix. Firstly, a complex Laplacian (Hermitian) matrix is constructed from the Gaussian-weighted distances and the difference of SIFT [10] angles between each pair of points in the two images to be matched. Matches are computed by comparing the complex eigenvectors of the Hermitian property matrices for the two point sets acquired from the two images. Secondly, we embed the complex modal structure within Carcassoni’s [12] iterative alignment method to render it more robust to rotation. Our method has been evaluated on both synthetic and real-world data.
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Haseeb, M., Hancock, E.R. (2011). Feature Point Matching Using a Hermitian Property Matrix. In: Pelillo, M., Hancock, E.R. (eds) Similarity-Based Pattern Recognition. SIMBAD 2011. Lecture Notes in Computer Science, vol 7005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24471-1_23
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DOI: https://doi.org/10.1007/978-3-642-24471-1_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24470-4
Online ISBN: 978-3-642-24471-1
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