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Disparity and optical flow partitioning using extended Potts priors

Disparity and optical flow partitioning using extended Potts priors
Disparity and optical flow partitioning using extended Potts priors
This paper addresses the problems of disparity and optical flow partitioning based on the brightness invariance assumption. We investigate new variational approaches to these problems with Potts priors and possibly box constraints. For the optical flow partitioning, our model includes vector-valued data and an adapted Potts regularizer. Using the notion of asymptotically level stable (als) functions, we prove the existence of global minimizers of our functionals. We propose a modified alternating direction method of multipliers. This iterative algorithm requires the computation of global minimizers of classical univariate Potts problems which can be done efficiently by dynamic programming. We prove that the algorithm converges both for the constrained and unconstrained problems. Numerical examples demonstrate the very good performance of our partitioning method.
0 minimization, ADMM-like algorithm, Disparity partitioning, Jump sparsity, Optical flow partitioning, Potts priors
2049-8764
43-62
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Fitschen, Jan Henrik
66a47635-37ef-4da8-b1a9-b2a5d3c580ae
Nikolova, Mila
f3374818-e03c-4f5d-8458-64ec9ce1d73c
Steidl, Gabriele
c61576e3-9691-455d-bf74-7014841fb0de
Storath, Martin
b31c105f-7f6e-4c5d-9f47-64aa721d4bbc
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Fitschen, Jan Henrik
66a47635-37ef-4da8-b1a9-b2a5d3c580ae
Nikolova, Mila
f3374818-e03c-4f5d-8458-64ec9ce1d73c
Steidl, Gabriele
c61576e3-9691-455d-bf74-7014841fb0de
Storath, Martin
b31c105f-7f6e-4c5d-9f47-64aa721d4bbc

Cai, Xiaohao, Fitschen, Jan Henrik, Nikolova, Mila, Steidl, Gabriele and Storath, Martin (2014) Disparity and optical flow partitioning using extended Potts priors. Information and Inference: A Journal of the IMA, 4 (1), 43-62. (doi:10.1093/imaiai/iau010).

Record type: Article

Abstract

This paper addresses the problems of disparity and optical flow partitioning based on the brightness invariance assumption. We investigate new variational approaches to these problems with Potts priors and possibly box constraints. For the optical flow partitioning, our model includes vector-valued data and an adapted Potts regularizer. Using the notion of asymptotically level stable (als) functions, we prove the existence of global minimizers of our functionals. We propose a modified alternating direction method of multipliers. This iterative algorithm requires the computation of global minimizers of classical univariate Potts problems which can be done efficiently by dynamic programming. We prove that the algorithm converges both for the constrained and unconstrained problems. Numerical examples demonstrate the very good performance of our partitioning method.

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More information

Accepted/In Press date: 19 October 2014
Published date: 31 December 2014
Keywords: 0 minimization, ADMM-like algorithm, Disparity partitioning, Jump sparsity, Optical flow partitioning, Potts priors

Identifiers

Local EPrints ID: 438615
URI: http://eprints.soton.ac.uk/id/eprint/438615
ISSN: 2049-8764
PURE UUID: f039d5e6-7412-4487-a763-33f030bccc04

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Date deposited: 18 Mar 2020 17:33
Last modified: 29 Jun 2020 16:30

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Contributors

Author: Xiaohao Cai
Author: Jan Henrik Fitschen
Author: Mila Nikolova
Author: Gabriele Steidl
Author: Martin Storath

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