Active contours without edges

By Tony F. Chan, Luminita A. Vese. 2001

9,863 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Modeling and Simulation, Signal Processing

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Citations in the corpus

Image Segmentation Using Deep Learning: A Survey (2020, methodology): “For example, inspired by the global energy formulation of [106], Chen et al.”
A Level Set Approach to Image Segmentation With Intensity Inhomogeneity (2016, neutral): “Many methods have been proposed to simplify or modify the functional [1], [8], which will be reviewed afterward.”
Non-local Deep Features for Salient Object Detection (2017, background): “(1) and (2), the most common unsupervised approaches to optimize these employ level set base curve evolution techniques [8, 42], generalized Bayesian criteria using the variational principle, and simulated annealing [51].”
Optimization Methods on Riemannian Manifolds and Their Application to Shape Space (2012, methodology): “) To illustrate the efficiency of Riemannian optimization in this context we consider the task of image segmentation via active contours without edges as proposed by Chan and Vese [8].”
An efficient local Chan-Vese model for image segmentation (2010, methodology): “Our local Chan–Vese (LCV) model is presented in Section 3.”
3D point cloud segmentation: A survey (2013, background): “It attracts a significant amount of work [10] [40] [27].”
Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models (2006, methodology): “In particular, we consider the two-phase, piecewise constant Mumford–Shah segmentation functional proposed by Chan and Vese in [7] and show that part of the minimization involved can be given a convex formulation.”
Visual Tracking: an Experimental Survey (2013, neutral): “Even though contour-based trackers provide more detailed shape and deformation of object as it is being tracked, there are severe difficulties in the initialization of the contour [11], in occlusions, and in the robustness against abrupt object motion.”
Fast Global Minimization of the Active Contour/Snake Model (2007, methodology): “We will show that our model, besides being independent of the initial condition, improves the model of Chan and Vese when the contrast between meaningful objects and the background is low.”
A convex relaxation approach for computing minimal partitions (2009, background): “[13]), convex relaxation and thresholding techniques which allow to compute optimal binary labelings [11], optimal continuous multi-labelings in the case of convex regularity terms [25] and calibration methods [1, 8].”
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction (2010, methodology): “Using a level set function, u, and a regularized Heaviside function, H (cid:15) , the optimal curve can be computed using the gradient ﬂow [18] for ﬁxed c 1 , and c 2 .”
Primal‐dual active set methods for Allen–Cahn variational inequalities with nonlocal constraints (2010, methodology): “This method was proposed by Kay and Tomasi [77] and is based on the level set approach by Chan and Vese [37].”
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation (2006, methodology): “…low-level criteria such as edge consistency (Malladi et al., 1995; Caselles et al., 1995; Kichenassamy et al., 1995), intensity homogeneity (Chan and Vese, 2001; Tsai et al., 2001), texture information (Paragios and Deriche, 2002; Rousson et al., 2003; Heiler and Schnörr, 2003; Brox and…”