Image Morphing in Deep Feature Spaces: Theory and Applications

Alexander Effland, Erich Kobler, Thomas Pock, Marko Rajkovic, Martin Rumpf

Research output: Working paperPreprint


This paper combines image metamorphosis with deep features. To this end, images are considered as maps into a high-dimensional feature space and a structure-sensitive, anisotropic flow regularization is incorporated in the metamorphosis model proposed by Miller, Trouvé, Younes and coworkers. For this model a variational time discretization of the Riemannian path energy is presented and the existence of discrete geodesic paths minimizing this energy is demonstrated. Furthermore, convergence of discrete geodesic paths to geodesic paths in the time continuous model is investigated. The spatial discretization is based on a finite difference approximation in image space and a stable spline approximation in deformation space, the fully discrete model is optimized using the iPALM algorithm. Numerical experiments indicate that the incorporation of semantic deep features is superior to intensity-based approaches.
Original languageEnglish
Publication statusPublished - 2019

Publication series e-Print archive
PublisherCornell University Library


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