Statistical region snake-based segmentation adapted to different physical noise models

Chesnaud, Christophe; Réfrégier, Philippe; Boulet, V.

doi:10.1109/34.809108

Cited by 248 publications

(138 citation statements)

References 26 publications

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“…Similarly, Gamma distributions can be used to model multiplicative noise, such as speckles (Aubert and Aujol 2008). More generally, data-fitting energies can be chosen from a large family of statistical models, such as the exponential family (EF), introduced to the image-processing community by Chesnaud et al (1999).…”

Section: Data-fitting Energiesmentioning

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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Section: Data-fitting Energiesmentioning

confidence: 99%

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

2013

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“…A detailed state of the art on region-based active contours can be found in . Let us briefly note that some authors do not compute the theoretical expression of the velocity field but choose a deformation of the curve that will make the criterion decrease (Chesnaud et al, 1999). Other authors (Zhu and Yuille, 1996;Chan and Vese, 2001;Paragios and Deriche, 2002) compute the theoretical expression of the velocity vector from the Euler-Lagrange equations.…”

Section: Problem Statementmentioning

confidence: 99%

Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures

et al. 2006

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Abstract. In this paper, we propose to focus on the segmentation of vectorial features (e.g. vector fields or color intensity) using region-based active contours. We search for a domain that minimizes a criterion based on homogeneity measures of the vectorial features. We choose to evaluate, within each region to be segmented, the average quantity of information carried out by the vectorial features, namely the joint entropy of vector components. We do not make any assumption on the underlying distribution of joint probability density functions of vector components, and so we evaluate the entropy using non parametric probability density functions. A local shape minimizer is then obtained through the evolution of a deformable domain in the direction of the shape gradient. The first contribution of this paper lies in the methodological approach used to differentiate such a criterion. This approach is mainly based on shape optimization tools. The second one is the extension of this method to vectorial data. We apply this segmentation method on color images for the segmentation of color homogeneous regions. We then focus on the segmentation of synthetic vector fields and show interesting results where motion vector fields may be separated using both their length and their direction. Then, optical flow is estimated in real video sequences and segmented using the proposed technique. This leads to promising results for the segmentation of moving video objects.

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“…Our approach has similarity to previous active contour based models. Chesnaud et al (1999) presented a statistical region based active contour adapted to different noise models using ML criterion. They proposed a general model for different statistical laws.…”

Section: Introductionmentioning

confidence: 99%

Mammography segmentation with maximum likelihood active contours

Rahmati

Adler

Hamarneh

2012

Medical Image Analysis

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Statistical region snake-based segmentation adapted to different physical noise models

Cited by 248 publications

References 26 publications

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

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

Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures

Mammography segmentation with maximum likelihood active contours

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