Definition of a Signal-to-Noise Ratio for Object Segmentation Using Polygonal MDL-Based Statistical Snakes

Goudail, François; Réfrégier, Philippe; Ruch, Olivier

doi:10.1007/978-3-540-45063-4_24

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…For an introduction to the EF in image segmentation, we refer to the works of Goudail et al (2003), Martin et al (2004), and Lecellier et al (2010) Lecellier et al (2010), in the sense that it is a one-parameter canonical EF with the identity function as its sufficient statistic (cf. Lecellier et al (2010) for the definition of the sufficient statistics vector).…”

Section: Generalized Linear Models (Glm)mentioning

confidence: 99%

Coupling Image Restoration and Segmentation: A Generalized Linear Model/Bregman Perspective

2013

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We introduce a new class of data-fitting energies that couple image segmentation with image restoration. These functionals model the image intensity using the statistical framework of generalized linear models. By duality, we establish an information-theoretic interpretation using Bregman divergences. We demonstrate how this formulation couples in a principled way image restoration tasks such as denoising, deblurring (deconvolution), and inpainting with segmentation. We present an alternating minimization algorithm to solve the resulting composite photometric/geometric inverse problem. We use Fisher scoring to solve the photometric problem and to provide asymptotic uncertainty estimates. We derive the shape gradient of our data-fitting energy and investigate convex relaxation for the geometric problem. We introduce a new alternating split-Bregman strategy to

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