A fast algorithm for image segmentation based on fuzzy region competition
DOI10.1007/s10444-011-9221-4zbMath1254.68335OpenAlexW1978870546MaRDI QIDQ1928070
Fangfang Dong, Chunxiao Liu, De-Xing Kong
Publication date: 2 January 2013
Published in: Advances in Computational Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10444-011-9221-4
level set methodimage segmentationsplit Bregman methodvariational modelsfuzzy region competitionJia and Zhao's algorithm
Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Fuzzy sets and logic (in connection with information, communication, or circuits theory) (94D05) PDEs in connection with information and communication (35Q94) PDEs in connection with computer science (35Q68)
Cites Work
- Nonlinear total variation based noise removal algorithms
- Convergence analysis of the Bregman method for the variational model of image denoising
- Constraints on deformable models: Recovering 3D shape and nonrigid motion
- Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
- A geometric model for active contours in image processing
- Image recovery via total variation minimization and related problems
- Geodesic active contours
- An algorithm for total variation minimization and applications
- A variational level set approach to multiphase motion
- Optimal approximations by piecewise smooth functions and associated variational problems
- Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
- Split Bregman Methods and Frame Based Image Restoration
- Active contours without edges
- An Iterative Regularization Method for Total Variation-Based Image Restoration
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