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
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Shape Gradients for Histogram Segmentation using Active Contours
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Gradient Vector Flow Fast Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical aerial image partitioning using level sets based on modified Chan-Vese model
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
MAC: Magnetostatic Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Statistic Based Region Segmentation with Automatic Scale Selection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Local Histogram Based Segmentation Using the Wasserstein Distance
International Journal of Computer Vision
Active contours driven by local Gaussian distribution fitting energy
Signal Processing
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Histogram based segmentation using Wasserstein distances
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Pattern Recognition Letters
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Active contours for tracking distributions
IEEE Transactions on Image Processing
RAGS: region-aided geometric snake
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Real-Time Algorithm for the Approximation of Level-Set-Based Curve Evolution
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
Hi-index | 0.10 |
A nonparametric local region-based active contour driven by a local histogram fitting energy is presented. The energy is defined in terms of an evolving curve and two fitting histograms that approximate the distribution of object and background locally through a truncated Gaussian kernel. The kernel width for computing the fitting histograms should be different on different pixels, since the same kernel width applied may cause local minima of the energy. Three inequalities are introduced to determine whether larger kernel width should be considered. We do not assume any distributions in the presented method. The method therefore belongs to a nonparametric local region based active contour, and it can segment the regions whose distribution is hard to be predefined. Experimental results show desirable performances of our method.