Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Shape Gradients for Histogram Segmentation using Active Contours
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Optimal Mass Transport for Registration and Warping
International Journal of Computer Vision
Shape preserving local histogram modification
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Local Histogram Based Segmentation Using the Wasserstein Distance
International Journal of Computer Vision
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
Image segmentation using histogram fitting and spatial information
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Measure based metrics for aggregated data
Intelligent Data Analysis
Variational color image segmentation via chromaticity-brightness decomposition
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Active contour model driven by local histogram fitting energy
Pattern Recognition Letters
Fast two-stage segmentation via non-local active contours in multiscale texture feature space
Pattern Recognition Letters
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In this paper, we propose a new nonparametric region-based active contour model for clutter image segmentation. To quantify the similarity between two clutter regions, we propose to compare their respective histograms using the Wasserstein distance. Our first segmentation model is based on minimizing the Wasserstein distance between the object (resp. background) histogram and the object (resp. background) reference histogram, together with a geometric regularization term that penalizes complicated region boundaries. The minimization is achieved by computing the gradient of the level set formulation for the energy. Our second model does not require reference histograms and assumes that the image can be partitioned into two regions in each of which the local histograms are similar everywhere.