An improved hybrid gradient variation level set method for image segmentation and bias correction
DOI10.1080/00207160.2015.1079625zbMath1359.62273OpenAlexW2283221602MaRDI QIDQ2958264
Hefeng Yin, Junfeng Cao, Xiao-Jun Wu
Publication date: 1 February 2017
Published in: International Journal of Computer Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207160.2015.1079625
Image analysis in multivariate analysis (62H35) Energy minimization in equilibrium problems in solid mechanics (74G65) Variational methods applied to PDEs (35A15) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Finite difference methods for boundary value problems involving PDEs (65N06)
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Cites Work
- Image segmentation with a Sobolev gradient method
- Variational level set methods for image segmentation based on both \(L^2\) and Sobolev gradients
- Sobolev active contours
- Active contours driven by local image fitting energy
- Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
- A multiphase level set framework for image segmentation using the Mumford and Shah model
- Active contours with free endpoints
- A novel active contour model for image segmentation using distance regularization term
- An efficient local Chan-Vese model for image segmentation
- Active contours without edges
- Distance Regularized Level Set Evolution and Its Application to Image Segmentation
- A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI
- Sobolev gradients and differential equations
- Steepest descent using smoothed gradients
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