Texture map: an effective representation for image segmentation
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Similarity Matches of Gene Expression Data Based on Wavelet Transform
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
A conditional random field approach to unsupervised texture image segmentation
EURASIP Journal on Advances in Signal Processing
MR image segmentation using phase information and a novel multiscale scheme
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Image segmentation using local spectral histograms and linear regression
Pattern Recognition Letters
Exploiting intensity inhomogeneity to extract textured objects from natural scenes
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Multiscale Texture Extraction with Hierarchical (BV,Gp,L2) Decomposition
Journal of Mathematical Imaging and Vision
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This paper addresses two challenging issues in unsupervised multiscale texture segmentation: determining adequate spatial and feature resolutions for different regions of the image, and utilizing information across different scales/resolutions. The center of a homogeneous texture is analyzed using coarse spatial resolution, and its border is detected using fine spatial resolution so as to locate the boundary accurately. The extraction of texture features is achieved via a multiresolution pyramid. The feature values are integrated across scales/resolutions adaptively. The number of textures is determined automatically using the variance ratio criterion. Experimental results on synthetic and real images demonstrate the improvement in performance of the proposed multiscale scheme over single scale approaches.