Results 1  10
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312
Active Contours without Edges
, 2001
"... In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, MumfordShah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy ..."
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Cited by 798 (36 self)
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In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, MumfordShah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "meancurvature flow"like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We will give a numerical algorithm using finite differences. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
An Algorithm for Total Variation Minimization and Applications
, 2004
"... We propose an algorithm for minimizing the total variation of an image, and provide a proof of convergence. We show applications to image denoising, zooming, and the computation of the mean curvature motion of interfaces. ..."
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Cited by 349 (9 self)
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We propose an algorithm for minimizing the total variation of an image, and provide a proof of convergence. We show applications to image denoising, zooming, and the computation of the mean curvature motion of interfaces.
A Nonlinear PrimalDual Method For Total VariationBased Image Restoration
, 1995
"... . We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity jruj in the definition of the TVnorm before we apply a linearization technique such as Newton ..."
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Cited by 162 (22 self)
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. We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity jruj in the definition of the TVnorm before we apply a linearization technique such as Newton's method. This is accomplished by introducing an additional variable for the flux quantity appearing in the gradient of the objective function. Our method can be viewed as a primaldual method as proposed by Conn and Overton [8] and Andersen [3] for the minimization of a sum of Euclidean norms. Experimental results show that the new method has much improved global convergence behaviour than the primal Newton's method. 1. Introduction. During some phases of the manipulation of an image some random noise and blurring is usually introduced. The presence of this noise and blurring makes difficult and inaccurate the latter phases of the image processing. The algorithms for noise removal and debl...
Mathematical Models for Local Nontexture Inpaintings
 SIAM J. Appl. Math
, 2002
"... Inspired by the recent work of Bertalmio et al. on digital inpaintings [SIGGRAPH 2000], we develop general mathematical models for local inpaintings of nontexture images. On smooth regions, inpaintings are connected to the harmonic and biharmonic extensions, and inpainting orders are analyzed. For i ..."
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Cited by 148 (30 self)
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Inspired by the recent work of Bertalmio et al. on digital inpaintings [SIGGRAPH 2000], we develop general mathematical models for local inpaintings of nontexture images. On smooth regions, inpaintings are connected to the harmonic and biharmonic extensions, and inpainting orders are analyzed. For inpaintings involving the recovery of edges, we study a variational model that is closely connected to the classical total variation (TV) denoising model of Rudin, Osher, and Fatemi [PhSG D, 60 (1992), pp. 259268]. Other models are also discussed based on the MumfordShah regularity [Comm. Pure Appl. Mathq XLII (1989), pp. 577685] and curvature driven di#usions (CDD) of Chan and Shen [J. Visual Comm. Image Rep., 12 (2001)]. The broad applications of the inpainting models are demonstrated through restoring scratched old photos, disocclusion in vision analysis, text removal, digital zooming, and edgebased image coding.
Aspects of total variation regularized L 1 function approximation
 SIAM J. Appl. Math
, 2005
"... Abstract. The total variation based image denoising model of Rudin, Osher, and Fatemi has been generalized and modified in many ways in the literature; one of these modifications is to use the L 1 norm as the fidelity term. We study the interesting consequences of this modification, especially from ..."
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Cited by 103 (7 self)
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Abstract. The total variation based image denoising model of Rudin, Osher, and Fatemi has been generalized and modified in many ways in the literature; one of these modifications is to use the L 1 norm as the fidelity term. We study the interesting consequences of this modification, especially from the point of view of geometric properties of its solutions. It turns out to have interesting new implications for data driven scale selection and multiscale image decomposition.
Analysis of Bounded Variation Penalty Methods for IllPosed Problems
 INVERSE PROBLEMS
, 1994
"... This paper presents an abstract analysis of bounded variation (BV) methods for illposed operator equations Au = z. Let T (u) def = kAu \Gamma zk 2 + ffJ(u); where the penalty, or "regularization", parameter ff ? 0 and the functional J(u) is the BV norm or seminorm of u, also known as the tota ..."
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Cited by 101 (1 self)
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This paper presents an abstract analysis of bounded variation (BV) methods for illposed operator equations Au = z. Let T (u) def = kAu \Gamma zk 2 + ffJ(u); where the penalty, or "regularization", parameter ff ? 0 and the functional J(u) is the BV norm or seminorm of u, also known as the total variation of u. Under mild restrictions on the operator A and the functional J(u), it is shown that the functional T (u) has a unique minimizer which is stable with respect to certain perturbations in the data z, the operator A, the parameter ff, and the functional J(u). In addition, convergence results are obtained which apply when these perturbations vanish and the regularization parameter is chosen appropriately.
Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
 SIAM Journal on Applied Mathematics
, 2004
"... Abstract. 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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Cited by 93 (5 self)
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Abstract. 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.
Euler's Elastica And Curvature Based Inpaintings
 SIAM J. Appl. Math
, 2002
"... Image inpainting is a special image restoration problem for which image prior models play a crucial role. Euler's elastica was first introduced by Mumford [21] to computer vision as a prior curve model. By functionalizing the elastica energy, Masnou and Morel [19] proposed an elastica based variatio ..."
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Cited by 89 (24 self)
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Image inpainting is a special image restoration problem for which image prior models play a crucial role. Euler's elastica was first introduced by Mumford [21] to computer vision as a prior curve model. By functionalizing the elastica energy, Masnou and Morel [19] proposed an elastica based variational inpainting model. The current paper is intended to contribute to the development of its mathematical foundation, and the study of its properties and connections to the earlier works of Bertalmio, Sapiro, Caselles, and Ballester [2] and Chan and Shen [6, 7]. A computational scheme based on numerical PDEs is presented, which allows the handling of topologically complex inpainting domains.
On the structure of spaces with Ricci curvature bounded below
 I, II, III, J. Differential Geom
, 1997
"... In this paper and in [12], [13], we study the structure of spaces, Y, which arepointed GromovHausdor limits of sequences, f(M n i �pi)g, of complete, connected Riemannian manifolds whose Ricci curvatures ..."
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Cited by 89 (8 self)
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In this paper and in [12], [13], we study the structure of spaces, Y, which arepointed GromovHausdor limits of sequences, f(M n i �pi)g, of complete, connected Riemannian manifolds whose Ricci curvatures
Using prior shapes in geometric active contours in a variational framework
 IJCV
, 2002
"... Abstract. In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the ..."
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Cited by 87 (3 self)
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Abstract. In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the segmented contour to the prior shape. The active contour is able to find boundaries that are similar in shape to the prior, even when the entire boundary is not visible in the image (i.e., when the boundary has gaps). A level set formulation of the active contour is presented. The existence of the solution to the energy minimization is also established. We also report experimental results of the use of this contour on 2d synthetic images, ultrasound images and fMRI images. Classical active contours cannot be used in many of these images.