Morphological Texture Analysis Using Optimization of Structuring Elements
This paper proposes a method of texture analysis using morphological size distribution. Our framework is based on the concept that a texture is described by estimation of primitive, size distribution of grains derived from the primitive, and spatial distribution of the grains. We concentrate on estimation of primitive using an assumption on grain size distribution. We assume a model that grains are derived from one primitive, and a uniform size distribution since we consider target textures containing grains of various sizes. Thus the structuring element used for the measurement of size distribution is optimized to obtain the most uniform size density function. The optimized structuring element is an estimate of the primitive under the assumption. Simulated annealing algorithm is employed for the optimization.
Unable to display preview. Download preview PDF.
- Ojala T. and Pietikäinen M., “Texture classification” in R.B. Fisher, ed., CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision. (http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/OJALA1/texclas.htm)
- Asano, A.: Texture Analysis Using Morphological Pattern Spectrum and Optimization of Structuring Elements. Proc. 10th International Conference on Image Analysis and Processing (1999) 209–214Google Scholar
- Asano, A., Miyagawa, M., and Fujio, M.: Texture Modelling by Optimal Gray Scale Structuring Elements using Morphological Pattern Spectrum. Proc. 15th International Conference on Pattern Recognition 3 (2000) 479–482Google Scholar
- Gimel’farb, G.: “Characteristic interaction structures in Gibbs texture modelling” in J. Blanc-Talon and D. Popescu, eds., Imaging and Vision Systems: Theory, Assessment and Applications. Nova Science Publishers (2001) 71–90Google Scholar
- Heijmans, H. J.A. M.: Morphological Image Operators. Academic Press (1994)Google Scholar
- Dougherty, E.R., Newell, J. T., and Pelz, J.B.: Morphological Texture-Based Maximuml-Likelihood Pixel Classification Based on Local Granulometric Moments. Pattern Recognition 25 (1992) 1181–1198Google Scholar
- Sand, F. and Dougherty, E.R.: Asymptotic granulometric mixing theorem: morphological estimation of sizing parameters and mixture proportions. Pattern Recognition 31 (1998) 53–61Google Scholar
- Sand, F. and Dougherty, E.R.: Robustness of granulometric moments. Pattern Recognition 32 (1999) 1657–1665Google Scholar
- Serra, J.: Image Analysis and Mathematical Morphology Academic Press (1982)Google Scholar
- Asano, A., Ohkubo, T., Muneyasu, M., and Hinamoto, T.: Texture Primitive Description Using Morphological Skeleton. Proc. International Symposium on Mathematical Morphology VI (2002) 101–108Google Scholar
- Asano, A., Endo, J., and Muraki C.: Multiprimitive Texture Analysis Using Cluster Analysis and Size Density Function. Proc. International Symposium on Mathematical Morphology VI (2002) 109–116 152Google Scholar