Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
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
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Non-local Regularization of Inverse Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Semi-supervised Segmentation Based on Non-local Continuous Min-Cut
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A Non-Local Fuzzy Segmentation Method: Application to Brain MRI
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing
IEEE Transactions on Image Processing
Texture segmentation via non-local non-parametric active contours
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Variational nonlocal image segmentation using split-Bregman
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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This article introduces a new image segmentation method that makes use of non-local comparisons between pairs of patches of features. A non-local energy is defined by summing the interactions between pairs of patches inside and outside the segmented domain. A maximum radius of interaction can be adapted to fit the amount of variation of the features inside and outside the region to be segmented. This non-local energy is minimized using a level set approach. The corresponding curve evolution defines a non-local active contour that converges to a local minimum of our energy. In contrast to previous segmentation methods, this approach only requires a local homogeneity of the features inside and outside the region to be segmented. This does not impose a global homogeneity as required by region-based segmentation methods. This comparison principle is also less sensitive to initialization than edge-based approaches. We instantiate this novel framework using patches of intensity or color values as well as Gabor features. This allows us to segment regions with smoothly varying intensity or colors as well as complicated textures with a spatially varying local orientation.