Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Histograms and Their Use for Recognition
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
Image description using joint distribution of filter bank responses
Pattern Recognition Letters
Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector
Information Sciences: an International Journal
Scale-space texture classification using combined classifiers
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
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Textures have an intrinsic multiresolution property due to their varying texel size. This suggests using multiresolution techniques in texture analysis. Recently linear scale space techniques along with multiple classifier systems have been proposed as an effective approach in texture classification especially at small sample sizes. However, linear scale space blurs and dislocates conceptually meaningful structures irrespective of the type of structures exist. To address these problems, we utilize nonlinear scale space by which important geometrical structures are preserved throughout the scale space construction. This adds to the discrimination power of the classification system at higher scales. We evaluate the effectiveness of this approach for texture classification in Brodatz dataset using multiple classifier systems and learning curves. Compared with the linear scale space, we obtain higher accuracy in texture classification utilizing the nonlinear scale space.