Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Region-based strategies for active contour models
International Journal of Computer Vision
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
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
Detailed Real-Time Urban 3D Reconstruction from Video
International Journal of Computer Vision
Continuous Global Optimization in Multiview 3D Reconstruction
International Journal of Computer Vision
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
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
Image segmentation by iterated region merging with localized graph cuts
Pattern Recognition
Iterated graph cuts for image segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Variational and PCA based natural image segmentation
Pattern Recognition
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In this paper, a new region-based active contour model, namely local region-based Chan-Vese (LRCV) model, is proposed for image segmentation. By considering the image local characteristics, the proposed model can effectively and efficiently segment images with intensity inhomogeneity. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a degraded CV model is proposed, whose segmentation result can be taken as the initial contour of the LRCV model. In addition, we regularize the level set function by using Gaussian filtering to keep it smooth in the evolution process. Experimental results on synthetic and real images show the advantages of our method in terms of both effectiveness and robustness. Compared with the well-know local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour.