Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Unsupervised texture segmentation with nonparametric neighborhood statistics
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Adaptive scale fixing for multiscale texture segmentation
IEEE Transactions on Image Processing
Morphology-based multifractal estimation for texture segmentation
IEEE Transactions on Image Processing
A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation
IEEE Transactions on Image Processing
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Extracting textured objects from natural scenes is a challenging task in computer vision The main difficulties arise from the intrinsic randomness of natural textures and the high-semblance between the objects and the background In this paper, we approach the extraction problem with a seeded region-growing framework that purely exploits the statistical properties of intensity inhomogeneity The pixels in the interior of potential textured regions are first found as texture seeds in an unsupervised manner The labels of the texture seeds are then propagated through their respective inhomogeneous neighborhoods, to eventually cover the different texture regions in the image Extensive experiments on a large variety of natural images confirm that our framework is able to extract accurately the salient regions occupied by textured objects, without any complicated cue integration and specific priors about objects of interest.