Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional

By Daniel Cremers, Florian Tischhäuser, Joachim Weickert, Christoph Schnörr. International Journal of Computer Vision, 2002

390 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/18305601724d262acb98f1c0130cf202293a80d7

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In Computer Vision and Pattern Recognition

Citations in the corpus

Image-based plant phenotyping with incremental learning and active contours (2014, background): “Although naturally one would consider introducing shape priors in deformable models and contour formulations (Cremers et al., 2002; Yuille et al., 1989), it would require defining and learning shape priors of all possible plant shapes.”
An algorithm for minimizing the Mumford-Shah functional (2009, methodology): “A quite popular class of algorithms is based on the level set method [20, 19] or splines [10].”
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation (2005, background): “As discussed in Cremers et al. (2002), minimizing this constraint enforces an equidistant spacing of control points which strongly improves the numerical stability.”
A binary level set model and some applications to Mumford-Shah image segmentation (2006, neutral): “approximations are thoroughly treated in [22]–[25].”
Edge and line oriented contour detection: State of the art (2011, background): “With respect to other vectorization algorithms [219], this one is numerically stable, can successfully deal with high-curvature points and topological changes [217], and has a natural implementation in the framework of level set.”
Level set based shape prior segmentation (2005, background): “There are also many works on shape prior segmentation in the literature [9, 10, 2, 4, 6, 20].”
A Bayesian model of shape and appearance for subcortical brain segmentation (2011, positive): “, 2001), where a scaled identity matrix is added to the covariance matrix estimate, and has been used in the context of shape models previously (Cremers et al., 2002).”
A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape (2007, methodology): “preserve well-defined normal vectors), one needs to introduce certain regriding mechanisms to avoid overlap of control points, for example by numerically resampling the marker points every few iterations, by imposing in the variational formulation a rubber-band like attraction between neighboring po”
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation (2006, background): “Applications of explicit statistical shape models in image segmentation were among others proposed in Grenander et al. (1991), Cootes et al. (1995), Cremers et al. (2002), Mio et al. (2004), Cremers et al. (2003).”