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 in:SIAM journal on applied mathematics 2006-01, Vol.66 (5), p.1632-1648
Main Authors: Chan, Tony F., Selim Esedoḡlu, Nikolova, Mila
Format: Article
Language:English
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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Description
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.
ISSN:0036-1399
1095-712X
DOI:10.1137/040615286