Towards Texture Classification in Real Scenes

Two new texture features, based on morphological scale-space processors are introduced. The new methods are shown to have good performance over a variety of tests. We demonstrate that if texture classifies are to be used in real world scenes, then the choice of test is critical and that Brodatz-like tests are unlikely to represent reality.

[1]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[2]  J. Andrew Bangham,et al.  Morphological scale-space preserving transforms in many dimensions , 1996, J. Electronic Imaging.

[3]  Paul Southam,et al.  Compact rotation-invariant texture classification , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[4]  Susan M. Astley,et al.  Model-based detection of spiculated lesions in mammograms , 1999, Medical Image Anal..

[5]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Chi-Man Pun,et al.  Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Sameer Singh,et al.  Texture Analysis Experiments with Meastex and Vistex Benchmarks , 2001, ICAPR.

[8]  B. Silverman,et al.  Wavelets: The Key to Intermittent Information? , 2000 .

[9]  N. Kingsbury Image processing with complex wavelets , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[11]  Pierre Chardaire,et al.  Multiscale Nonlinear Decomposition: The Sieve Decomposition Theorem , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Matti Pietikäinen,et al.  Outex - new framework for empirical evaluation of texture analysis algorithms , 2002, Object recognition supported by user interaction for service robots.