Abstract
Effective variational models exist for either image segmentation or image registration for a given class of problems, though robustness is a longstanding issue. This paper proposes a new and effective variational model that aims to segment a pair of images through a joint model with registration, with the advantage of only requiring geometric prior information on one image (instead of two images) and obtaining selective segmentation on both images. Moreover we develop a deep image prior based learning algorithm to achieve the same segmentation and registration results by dropping the regularisation terms from the loss function. Numerical experiments show quality results obtained from the new approach.
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Burrows, L., Chen, K., Torella, F. (2021). A Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration. In: Elmoataz, A., Fadili, J., Quéau, Y., Rabin, J., Simon, L. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2021. Lecture Notes in Computer Science(), vol 12679. Springer, Cham. https://doi.org/10.1007/978-3-030-75549-2_33
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