Abstract
Image registration, especially the quantification of image similarity, is an important task in image processing. Various approaches for the comparison of two images are discussed in the literature. However, although most of these approaches perform very well in a two image scenario, an extension to a multiple images scenario deserves attention. In this article, we discuss and compare registration methods for multiple images. Our key assumption is, that information about the singular values of a feature matrix of images can be used for alignment. We introduce, discuss and relate three recent approaches from the literature: the Schatten q-norm based \(\mathrm {S}q\mathrm {N}\) distance measure, a rank based approach, and a feature volume based approach. We also present results for typical applications such as dynamic image sequences or stacks of histological sections. Our results indicate that the \(\mathrm {S}q\mathrm {N}\) approach is in fact a suitable distance measure for image registration. Moreover, our examples also indicate that the results obtained by \(\mathrm {S}q\mathrm {N}\) are superior to those obtained by its competitors.
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The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany in the framework of MED4D (project number 05M16FLA).
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Brehmer, K., Aggrawal, H.O., Heldmann, S., Modersitzki, J. (2019). Variational Registration of Multiple Images with the SVD Based \(\mathrm {S}q\mathrm {N}\) Distance Measure. In: Lellmann, J., Burger, M., Modersitzki, J. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2019. Lecture Notes in Computer Science(), vol 11603. Springer, Cham. https://doi.org/10.1007/978-3-030-22368-7_20
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DOI: https://doi.org/10.1007/978-3-030-22368-7_20
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