International Journal of Computer Vision
Texture segmentation using wavelet transform
Pattern Recognition Letters
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
A New Diffusion-Based Variational Model for Image Denoising and Segmentation
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Local Histogram Based Segmentation Using the Wasserstein Distance
International Journal of Computer Vision
Image segmentation based on GrabCut framework integrating multiscale nonlinear structure tensor
IEEE Transactions on Image Processing
Unsupervised hierarchical image segmentation with level set and additive operator splitting
Pattern Recognition Letters
Histogram based segmentation using Wasserstein distances
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
SIAM Journal on Imaging Sciences
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
IEEE Transactions on Image Processing
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
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In this paper, a new non-local active contour model is proposed for fast unsupervised segmentation of texture images. Under our framework, problems of texture description are addressed in a texture feature space. Then, the texture features are adaptively represented across scales and their homogeneities are efficiently measured by Wasserstein metric. With total variation regularization, an external energy including a non-local term and a global term is introduced into our energy functional, which can integrate non-local patch interactions with region homogeneities inside or outside the evolving contours. Our model proportionally reaches the balance between local and global homogeneities of features and exactly extracts meaningful objects. Finally, the segmentation approach is split into two stages, coarse segmentation for fast location in the coarse-scale space and accurate segmentation for bias correction in the fine-scale space. And the two segmentation problems are reformulated into the convex optimization framework, providing a global minimizer to our active contour model. Segmentation results of the synthetic and real-world images show that our model can accurately segment object regions in a fast way.