Flux maximizing geometric flows

By Alexander Vasilevskiy, Kaleem Siddiqi. Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2001

474 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c87638019dd6eb7490622c38188e42fd92537406

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In Biophysics

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Active contours with selective local or global segmentation: A new formulation and level set method (2010, background): “…University, Hong Kong, China b Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, People’s Republic of China a r t i c l e i n f o Article history: Received 26 June 2008 Received in revised form 18 October 2009…”
Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation (2010, methodology): “Most attempts at automation have involved hand-designed ﬁltering architectures with little machine learning (Vasilevskiy & Siddiqi, 2002; Al-Kofahi et al., 2002; Jurrus et al., 2009; Mishchenko, 2009; Andres et al., 2008).”
A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI (2011, unknown): “Existing level set methods for image segmentation can be categorized into two major classes: region-based models [4], [10], [17], [18], [20], [22] and edge-based models [3], [7], [8], [12], [21].”
Graph cut based image segmentation with connectivity priors (2008, background): “Alternative segmentation models One approach to overcome the shrinking bias is to add flux of some vector field to the model [10, 25, 12, 15].”
Global Optimization for Shape Fitting (2007, background): “This data-fit functional does not bias a reconstructed surface to any particular shape but it can be combined with appropriate shape priors (smoothness/regularization), volumetric occupancy data, or other terms (e.g. photoconsistency).”
Minimization of Region-Scalable Fitting Energy for Image Segmentation (2008, background): “Existing active contour models can be categorized into two major classes: edge-based models [3], [10]–[12], [18], [19], [28], [30], and region-based models [4], [23], [25]–[27], [29].”
Segmentation of retinal blood vessels using the radial projection and semi-supervised approach (2011, unknown): “[18] to segment narrow elongated vessels.”
Active contours driven by local image fitting energy (2010, methodology): “Generally speaking, the existing ACM methods can be classified into two types: edge-based models [1–4,6,9,14,19,20,23] and region-based models [5,7,8,10,11,16–18,21].”