Selecting the structuring element for morphological texture classification

This paper deals with a concrete aspect of texture classification: the choice of a good structuring element (SE) when the texture features used for classification are obtained from morphological granulometries. First, a granulometry is defined from the morphological opening of the texture using a convex and compact subset containing the origin as SE. Then, some usual distributional descriptors (mean, variance, skewness and kurtosis) of the granulometric size distribution are used as texture features. The main point of the paper is the choice of a good SE from the point of view of texture classification. A methodology is explained and software has been developed that helps in such a choice, for any given criterion for the quality of the classification.

[1]  B. Ripley.  Statistics, images, and pattern recognition , 1986 .

[2]  Edward R. Dougherty.  Euclidean gray-scale granulometries: Representation and umbra inducement , 2004, Journal of Mathematical Imaging and Vision.

[3]  Akira Asano.  Texture analysis using morphological pattern spectrum and optimization of structuring elements , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[4]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[5]  C. H. Chen,et al.  Handbook of Pattern Recognition and Computer Vision , 1993 .

[6]  F. Prêteux,et al.  Off-line signature verification by local granulometric size distributions , 1997 .

[7]  Guillermo Ayala,et al.  Spatial Size Distributions: Applications to Shape and Texture Analysis , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[9]  Robert Azencott,et al.  Texture Classification Using Windowed Fourier Filters , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Edward R. Dougherty,et al.  Asymptotic granulometric mixing theorem: Morphological estimation of sizing parameters and mixture proportions , 1998, Pattern Recognit..

[11]  E. Dougherty,et al.  Gray-scale morphological granulometric texture classification , 1994 .

[12]  Sven Loncaric,et al.  A survey of shape analysis techniques , 1998, Pattern Recognit..

[13]  Jeff B. Pelz,et al.  Image Segmentation By Local Morphological Granulometries , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[14]  Fritz Albregtsen,et al.  New texture features based on the complexity curve , 1999, Pattern Recognit..

[15]  Guillermo Ayala,et al.  A random set view of texture classification , 2018, IEEE Trans. Image Process..

[16]  Michael Spann,et al.  Texture feature performance for image segmentation , 1990, Pattern Recognit..

[17]  Anil K. Jain,et al.  Is there any texture in the image? , 1996, Proceedings of 13th International Conference on Pattern Recognition.

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

[19]  Petros Maragos,et al.  Pattern Spectrum and Multiscale Shape Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Jeff B. Pelz,et al.  Morphological texture-based maximum-likelihood pixel classification based on local granulometric moments , 1992, Pattern Recognit..

[21]  Krishnamoorthy Sivakumar,et al.  Discrete morphological size distributions and densities: estimation techniques and applications , 1997, J. Electronic Imaging.

[22]  G. Ayala,et al.  Granulometric moments and corneal endothelium status , 2001, Pattern Recognit..

[23]  H. Heijmans.  Morphological image operators , 1994 .

[24]  Krishnamoorthy Sivakumar,et al.  Morphologically Constrained GRFs: Applications to Texture Synthesis and Analysis , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Jia-Hong Lee,et al.  Texture classification using fuzzy uncertainty texture spectrum , 1998, Neurocomputing.