Abstract
We study a narrow band type algorithm to solve a discrete formulation of the convex relaxation of energy functionals with total variation regularization and nonconvex data terms. We prove that this algorithm converges to a local minimum of the original nonlinear optimization problem. We illustrate the algorithm with experiments for disparity computation in stereo and a multilabel segmentation problem, and we check experimentally that the energy of the local minimum is very near to the energy of the global minimum obtained without the narrow band type method.
Submitted Version (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
