Agile multi-scale decompositions for automatic image registration

James M. Murphy; Omar Navarro Leija; Jacqueline Le Moigne

doi:10.1117/12.2222182

17 May 2016 Agile multi-scale decompositions for automatic image registration

James M. Murphy, Omar Navarro Leija, Jacqueline Le Moigne

Author Affiliations +

Proceedings Volume 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII; 984011 (2016) https://doi.org/10.1117/12.2222182
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

In recent works, the first and third authors developed an automatic image registration algorithm based on a multiscale hybrid image decomposition with anisotropic shearlets and isotropic wavelets. This prototype showed strong performance, improving robustness over registration with wavelets alone. However, this method imposed a strict hierarchy on the order in which shearlet and wavelet features were used in the registration process, and also involved an unintegrated mixture of MATLAB and C code. In this paper, we introduce a more agile model for generating features, in which a flexible and user-guided mix of shearlet and wavelet features are computed. Compared to the previous prototype, this method introduces a flexibility to the order in which shearlet and wavelet features are used in the registration process. Moreover, the present algorithm is now fully coded in C, making it more efficient and portable than the mixed MATLAB and C prototype. We demonstrate the versatility and computational efficiency of this approach by performing registration experiments with the fully-integrated C algorithm. In particular, meaningful timing studies can now be performed, to give a concrete analysis of the computational costs of the flexible feature extraction. Examples of synthetically warped and real multi-modal images are analyzed.

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James M. Murphy, Omar Navarro Leija, and Jacqueline Le Moigne "Agile multi-scale decompositions for automatic image registration", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 984011 (17 May 2016); https://doi.org/10.1117/12.2222182

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