English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper addresses the problems of disparity and optical flow partitioningbased on the brightness invariance assumption. We investigate new variationalapproaches to these problems with Potts priors and possibly box constraints.For the optical flow partitioning, our model includes vector-valued data and anadapted Potts regularizer. Using the notation of asymptotically level stablefunctions we prove the existence of global minimizers of our functionals. Wepropose a modified alternating direction method of minimizers. This iterativealgorithm requires the computation of global minimizers of classical univariatePotts problems which can be done efficiently by dynamic programming. We provethat the algorithm converges both for the constrained and unconstrainedproblems. Numerical examples demonstrate the very good performance of ourpartitioning method.

Disparity and Optical Flow Partitioning Using Extended Potts Priors