Shape Priors for Level Set Representations

By Mikaël Rousson, Nikos Paragios. European Conference on Computer Vision, 2002

607 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/47272d33efa13809d26d3f4222a696acc17ecf30

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation (2006, methodology): “Rousson and Paragios (2002) and Rousson et al. (2004) impose shape information into the the variational formulation of the level set scheme, either by a model of local (spatially independent) Gaussian fluctuations around a mean level set function or by global deformation modes along the lines of…”
A comparative study of deformable contour methods on medical image segmentation (2008, methodology): “The methods in [32,33,19] integrate the a priori knowledge of the object shape into the level set formulation in order to constrain the contour deformation within an admissible range.”
An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, background): “In order to tackle these limitations, high level shape priors are introduced to level set models [39], [40] for better segmentation of partially occluded objects.”
Integral Invariants for Shape Matching (2006, background): “Recently, shape dissimilarity measures and statistical shape models have been formulated on the basis of the level set representation [45] , [86], [69], [20], [19].”
Star Shape Prior for Graph-Cut Image Segmentation (2008, methodology): “Using shape priors to improve segmentation has been investigated in the level set and curve evolution frameworks [18–21].”
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004 (2004, neutral): “Of note, this probability model bears resemblance to the stochastic framework introduced in [ 10 ] for the construction of prior shape models.”
Dynamical statistical shape priors for level set-based tracking (2006, background): “Rousson et al. [27], [28] suggested introducing shape information on the variational level, while Chen et al. [6] imposed shape constraints directly on the contour given by the zero level of the embedding function.”
TouchCut: Fast image and video segmentation using single-touch interaction (2014, methodology): “Finally, our proposed model also utilizes the geometric cue implied in single-touch input, that users typically touch image in close vicinity to the geometric center of the desired object.”
Active unsupervised texture segmentation on a diffusion based feature space (2003, methodology): “There are many techniques proposed in the literature, some of them including priors on the shape of objects [12, 7, 22], others, in the field of texture segmentation, use learned models of textures to retrieve them during the segmentation process [18, 4, 14].”
Using Prior Shapes in Geometric Active Contours in a Variational Framework (2002, methodology): “Prior statistical shape information is also used to control a total deformation of the active contour formed by global transformation (e.g. rotation, scaling, and traslation) plus a local deformation (see Paragios, 2002; Rousson, 2002; Satto, 2002).”
A level set approach for shape-driven segmentation and tracking of the left ventricle (2003, methodology): “At the same time, accou nting for prior shape knowledge - a significant limitation - was recently addressed in various forms [2], [7], [13].”
Level set based shape prior segmentation (2005, background): “In [13, 15], Paragios et al. represented a shape via a signed distance function – a special level set function [12].”
A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape (2007, background): “proposed variational integrations of the shape prior [78, 79] based on the assumption of a Gaussian distribution.”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “shape model is incorporated into the level set framework [236] to segment overlapping nuclei and lymphocytes in prostate and breast cancer histopathological images; in [74], the shape prior constraint [235] is integrated in the distance regularized level set model [237], and thus, the method can eff”