Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Generalized Pattern Spectra Sensitive to Spatial Information
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A comparative study on multivariate mathematical morphology
Pattern Recognition
On lexicographical ordering in multivariate mathematical morphology
Pattern Recognition Letters
Knowledge from markers in watershed segmentation
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Extending morphological covariance
Pattern Recognition
Comparative study of moment based parameterization for morphological texture description
Journal of Visual Communication and Image Representation
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Morphological covariance, one of the most frequently employed texture analysis tools offered by mathematical morphology, makes use of the sum of pixel values, i.e. “volume” of its input. In this paper, we investigate the potential of alternative measures to volume, and extend the work of Wilkinson (ICPR'02) in order to obtain a new covariance operator, more sensitive to spatial details, namely the spatial covariance. The classification experiments are conducted on the publicly available Outex 14 texture database, where the proposed operator leads not only to higher classification scores than standard covariance, but also to the best results reported so far for this database when combined with an adequate illumination invariance model.