Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Dictionary learning algorithms for sparse representation
Neural Computation
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Image segmentation using a multilayer level-set approach
Computing and Visualization in Science
Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization
SIAM Journal on Imaging Sciences
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Curvelet-based geodesic snakes for image segmentation with multiple objects
Pattern Recognition Letters
Fuzzy region competition: a convex two-phase segmentation framework
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction
Journal of Scientific Computing
A Multiphase Image Segmentation Method Based on Fuzzy Region Competition
SIAM Journal on Imaging Sciences
Global Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approach
International Journal of Computer Vision
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A binary level set model and some applications to Mumford-Shah image segmentation
IEEE Transactions on Image Processing
The Equivalence of Half-Quadratic Minimization and the Gradient Linearization Iteration
IEEE Transactions on Image Processing
Nonconvex sparse regularizer based speckle noise removal
Pattern Recognition
Variational and PCA based natural image segmentation
Pattern Recognition
Local joint entropy based non-rigid multimodality image registration
Pattern Recognition Letters
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We present a new variational model for the soft multiphase image segmentation. In the model, we introduce a nonconvex regularizer on the membership functions which are used as indicators of different homogeneous regions. The nonconvex regularizer performs better than the usual convex ones in that (i) it well preserves geometric shapes of the homogeneous regions, and (ii) it protects edges from oversmoothing which is a common drawback of the convex regularizer. To solve the nonconvex minimization problem, we design a new fast alternative iteration algorithm, which is robust to the setting of the parameters in the model. We conduct comprehensive experiments to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for image segmentation. The algorithm achieves more accurate results compared to other well-known convex variational methods for image segmentation.