A First Derivative Potts Model for Segmentation and Denoising Using ILP

Ruobing Shen (), Gerhard Reinelt and Stephane Canu
Additional contact information
Ruobing Shen: Heidelberg University
Gerhard Reinelt: Heidelberg University
Stephane Canu: Normandie University, INSA Rouen

A chapter in Operations Research Proceedings 2017, 2018, pp 53-59 from Springer

Abstract: Abstract Unsupervised image segmentation and denoising are two fundamental tasks in image processing. Usually, graph based models such as multicut are used for segmentation and variational models are employed for denoising. Our approach addresses both problems at the same time. We propose a novel ILP formulation of the first derivative Potts model with the $$\ell _1$$ data term, where binary variables are introduced to deal with the $$\ell _0$$ norm of the regularization term. The ILP is then solved by a standard off-the-shelf MIP solver. Numerical experiments are compared with the multicut problem.

Keywords: Image segmentation; Denoising; Potts model; Integer linear programming; Multicut (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-89920-6_8

Ordering information: This item can be ordered from
http://www.springer.com/9783319899206

DOI: 10.1007/978-3-319-89920-6_8

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().