On active contour models and balloons
CVGIP: Image Understanding
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Comparing Offset Curve Approximation Methods
IEEE Computer Graphics and Applications
International Journal of Computer Vision
Generalized Gradients: Priors on Minimization Flows
International Journal of Computer Vision
Journal of Mathematical Imaging and Vision
Local Statistic Based Region Segmentation with Automatic Scale Selection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
On the statistical interpretation of the piecewise smooth Mumford-Shah functional
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
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Global region-based active contours, like the Chan-Vese model, often make strong assumptions on the intensity distributions of the searched object and background, preventing their use in natural images. We introduce a more flexible local region energy achieving a trade-off between local features of gradient-like terms and global region features. Relying on the theory of parallel curves, we define our region term using constant length lines normal to the contour. Mathematical derivations are performed on an explicit curve, leading to a form allowing efficient implementation on a parametric snake. However, we provide implementations on both explicit and implicit contours.