Fast Global Minimization of the Active Contour/Snake Model

By Xavier Bresson, Selim Esedoglu, Pierre Vandergheynst, Jean-Philippe Thiran, Stanley J. Osher. Journal of Mathematical Imaging and Vision, 2007

958 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/47761b17aa40b0b08dc725b07634e95ba28b911e

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Citations in the corpus

A Level Set Approach to Image Segmentation With Intensity Inhomogeneity (2016, positive): “In this section, we compare our method with the CV model [1], the GCV model [21],1 the LRB model [9],2 the LIC model [11], and the LBF model [10],3 which”
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction (2010, methodology): “(Left) Minimization using the regularized dual projection algorithm [11].”
Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation (2015, background): “Moreover, Bresson et al. [52] proposed the convex relaxed active contour model which can achieve desirable global optimal.”
A Globally Optimal Algorithm for Robust TV-L1 Range Image Integration (2007, methodology): “The homogeneous total variation regularization can be replaced by a weighted TV-regularization [4], which enables an efficient solution procedure for the geodesic active contour model [7] (a) f i ∈ {−1, 1}, δ = 0 (b) f i ∈ [−1, 1], δ > 0 Figure 6.”
An Improved Algorithm for TV-L 1 Optical Flow (2009, background): “The edge preserving nature of total variation can be enhanced, if a suitable weighted TV-norm/active contour model is applied [8].”
An algorithm for minimizing the Mumford-Shah functional (2009, background): “Alternatively the level set framework can be replaced by graph cut algorithms [14] or convex relaxation approaches [9, 7].”
REMODE: Probabilistic, monocular dense reconstruction in real time (2014, background): “where λ is a free parameter controlling the trade-off between the data term and the regularizer, and G(u) is a weighting function related to the “G-Weighted Total Variation”, introduced in [21] in the context of image segmentation.”
Autonomous, Vision‐based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle (2016, background): “(2011), and Wendel, Maurer, Graber, Pock, & Bischof (2012) argued that, similar to other computer vision problems, such as image denoising (Rudin, Osher, & Fatemi, 1992) and optical flow estimation (Werlberger, Pock, & Bischof, 2010), a smoothing step is required in order to deal with noise and spur”
A Duality Based Approach for Realtime TV-L1 Optical Flow (2007, background): “The edge preserving nature of total variation can be enhanced, if a suitable weighted TV-norm/active contour model is applied [6].”