Automatic prior shape selection for image edge detection with modified Mumford-Shah model
DOI10.1016/j.camwa.2019.09.021zbMath1460.94013OpenAlexW2978463336MaRDI QIDQ2004627
Jing Qin, Zhimei Huo, Yilin Li, Yu-Ying Shi
Publication date: 7 October 2020
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.camwa.2019.09.021
ADMMedge detectionfixed-point iterative algorithmautomatic shape selectionmodified Mumford-Shah modelprior shape
Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Detection theory in information and communication theory (94A13)
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Cites Work
- A fast edge detection algorithm using binary labels
- Edge detection by adaptive splitting
- Extracting grain boundaries and macroscopic deformations from images on atomic scale
- Nonparametric shape priors for active contour-based image segmentation
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- An algorithm for total variation minimization and applications
- Numerical minimization of a second-order functional for image segmentation
- Fast dual minimization of the vectorial total variation norm and applications to color image processing
- Geometry of total variation regularized \(L^p\)-model
- Acceleration methods for image restoration problem with different boundary conditions
- Convergence and sparsity of Lasso and group Lasso in high-dimensional generalized linear models
- Automatic Prior Shape Selection for Image Segmentation
- Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers
- Optimal approximations by piecewise smooth functions and associated variational problems
- A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
- Approximation of functional depending on jumps by elliptic functional via t-convergence
- Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
- Split Bregman Methods and Frame Based Image Restoration
- Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models
- Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model
- Image Selective Smoothing and Edge Detection by Nonlinear Diffusion
- Singularity detection and processing with wavelets
- Image Selective Smoothing and Edge Detection by Nonlinear Diffusion. II
- Iterative Methods for Total Variation Denoising
- On Two Multigrid Algorithms for Modeling Variational Multiphase Image Segmentation
- An Iterative Regularization Method for Total Variation-Based Image Restoration
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