Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A level set approach for computing solutions to incompressible two-phase flow
Journal of Computational Physics
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
A Level Set Model for Image Classification
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Multiregion Level-Set Partitioning of Synthetic Aperture Radar Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
An active contour model for image segmentation based on elastic interaction
Journal of Computational Physics
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
Active contours driven by local Gaussian distribution fitting energy
Signal Processing
Active contours driven by local image fitting energy
Pattern Recognition
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Information Sciences: an International Journal
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
Variational and Level Set Methods in Image Segmentation
Variational and Level Set Methods in Image Segmentation
Improving feature space based image segmentation via density modification
Information Sciences: an International Journal
Efficient visual tracking using particle filter with incremental likelihood calculation
Information Sciences: an International Journal
Completely Convex Formulation of the Chan-Vese Image Segmentation Model
International Journal of Computer Vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model
IEEE Transactions on Image Processing
Minimization of Region-Scalable Fitting Energy for Image Segmentation
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
Multiscale roughness measure for color image segmentation
Information Sciences: an International Journal
A novel fuzzy Dempster-Shafer inference system for brain MRI segmentation
Information Sciences: an International Journal
Hi-index | 0.07 |
In this paper, we propose an active contour model and its corresponding algorithms with detailed implementation for image segmentation. In the proposed model, the local and global region fitting energies are described by the combination of the local and global Gaussian distributions with different means and variances, respectively. In this combination, we increase a weighting coefficient by which we can adjust the ratio between the local and global region fitting energies. Then we present an algorithm for implementing the proposed model directly. Considering that, in practice, the selection of the weighting coefficient is troublesome, we present a modified algorithm in order to overcome this problem and increase the flexibility. By adaptively updating the weighting coefficient and the time step with the contour evolution, this algorithm is less sensitive to the initialization of the contour and can speed up the convergence rate. Besides, it is robust to the noise and can be used to extract the desired objects. Experiment results demonstrate that the proposed model and its algorithms are effective with application to both the synthetic and real-world images.