Active contours without edges

By C. Corsi. 2013

1,149 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “For plant segmentation from complicated background using parts of the Ara2012 dataset, Minervini et al. (2014) used a new multi-channel active contour based on Chan and Vese (2001) with probabilistic priors on plant appearance.”
Deep Level Sets for Salient Object Detection (2017, methodology): “To solve this problem, the Variational Level Set Methods [8, 49, 28] are proposed to derive the evolutional PDE directly from a certain energy function.”
Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment (2011, neutral): “Sample results of C-V active contour [39], [41].”
A visual analytics approach to understanding cycling behaviour (2012, neutral): “The final breakup mode ( We >350) is called catastrophic and is attributed to drop instabilities such as Rayleigh-Taylor (R-T) and Kelvin-Helmholtz (K-H).”
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (2017, methodology): “Among the many approaches for drusen segmentation [2,4,6,8], we identified the method by Chen et al.”
Deep Neural Networks Motivated by Partial Differential Equations (2018, methodology): “tributions. An incomplete list of seminal works includes optical ow models for motion estimation [26], nonlinear diusion models for ltering of images [38], variational methods for image segmentation [36, 1, 8], and nonlinear edge-preserving denoising [42]. A standard step in PDE-based data processin”
A nonparametric statistical method for image segmentation using information theory and curve evolution (2005, positive): “…which provide the basis for a variety of more recent methods, include boundary-based segmentation such as Canny edge detection [5], region-based segmentation such as region growing [2], [25], and global optimization approaches such as those based on the Mumford-Shah functional [28], [9], [39].”
A survey of MRI-based brain tumor segmentation methods (2014, background): “Some researchers think that the external energy of snake function is only zero at the edges and positive in homogeneous regions [115] .”
State of the art survey on MRI brain tumor segmentation. (2013, background): “The external energy of snake function is only positive in homogeneous regions, and zero at the edges [92].”
A Survey and Comparison of Discrete and Continuous Multi-label Optimization Approaches for the Potts Model (2013, background): “In a Bayesian MAP inference approach, fi = − log P(I, x |Ωi ) corresponds to the negative logarithm of the conditional probability for observing a specific color at the given location within region Ωi —see also Cremers et al. (2007) and Nieuwenhuis and Cremers (2012).”
A Nonrigid Kernel-Based Framework for 2D-3D Pose Estimation and 2D Image Segmentation (2011, positive): “Consequently, an alternative characterization , based on so-called region-based methods, is to assume that the " object " and " background " possess differing image statistics (see [8], [32], [30]).”
Computer Methods and Programs in Biomedicine (neutral): “Region-based deformable models were ﬁrst introduced in [181] by considering the deformable curve S as moving under image foreground and background region constraints.”