Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models

We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes.

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Bibliographic Details
Published inSIAM journal on applied mathematics Vol. 66; no. 5; pp. 1632 - 1648
Main Authors Chan, Tony F., Selim Esedoḡlu, Nikolova, Mila
Format Journal Article
LanguageEnglish
Published Philadelphia Society for Industrial and Applied Mathematics 01.01.2006
Subjects
Algorithms
Applied mathematics
Approximation
Computer vision
Eigenfunctions
Energy
Geometric shapes
Histograms
Initial guess
Local minimum
Mathematical functions
Mathematical minima
Noise
Optimization
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Summary:We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0036-1399
1095-712X
DOI:10.1137/040615286