Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Optimal Mass Transport for Registration and Warping
International Journal of Computer Vision
Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures
Journal of Mathematical Imaging and Vision
Non-local Regularization of Inverse Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Assignment Problems
Local Histogram Based Segmentation Using the Wasserstein Distance
International Journal of Computer Vision
Active contours driven by local Gaussian distribution fitting energy
Signal Processing
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
Variational color image segmentation via chromaticity-brightness decomposition
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Wasserstein barycenter and its application to texture mixing
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing
IEEE Transactions on Image Processing
Fast two-stage segmentation via non-local active contours in multiscale texture feature space
Pattern Recognition Letters
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This article introduces a novel active contour model that makes use of non-parametric estimators over patches for the segmentation of textured images. It is based on an energy that enforces the homogeneity of these statistics. This smoothness is measured using Wasserstein distances among discretized probability distributions that can handle features in arbitrary dimension. It is thus usable for the segmentation of color images or other high dimensional features. The Wasserstein distance is more robust than traditional pointwise statistical metrics (such as the Kullback-Leibler divergence) because it takes into account the relative distances between modes in the distributions. This makes the corresponding energy robust and does not require any smoothing of the statistical estimators. To speed-up the computational time, we propose an alternative metric that retains the main qualities of the Wasserstein distance, while being faster to compute. It aggregates 1-D Wasserstein distances over a set of directions, and thus benefits from the simplicity of 1-D statistical metrics while being able to discriminate high dimensional features. We show numerical results that instantiate this novel framework using grayscale and color values distributions. This allows us to segment regions with smoothly varying intensities or colors as well as complicated textures.