Morphological granulometry for classification of evolving and ordered texture images.

Mahmuda Khatun, Alison Gray, Stephen Marshall

Research output: Contribution to conferencePaper

3 Citations (Scopus)
91 Downloads (Pure)


In this work we investigate the use of morphological granulometric moments as texture descriptors to predict time or class of texture images which evolve over time or follow an intrinsic ordering of textures. A cubic polynomial regression was used to model each of several granulometric moments as a function of time or class. These models are then combined and used to predict time or class. The methodology was developed on synthetic images of evolving textures and then successfully applied to classify a sequence of corrosion images to a point on an evolution time scale. Classification performance of the new regression approach is compared to that of linear discriminant analysis, neural networks and support vector machines. We also apply our method to images of black tea leaves, which are ordered according to granule size, and very high classification accuracy was attained compared to existing published results for these images. It was also found that granulometric moments provide much improved classification compared to grey level co-occurrence features for shape-based texture images.
Original languageEnglish
Number of pages5
Publication statusPublished - Aug 2011
Event 19th European Signal Processing Conference -EUSIPCO 2011 - Barcelona, Spain
Duration: 29 Aug 20112 Sep 2011


Conference 19th European Signal Processing Conference -EUSIPCO 2011


  • morphological granulometric moments
  • cubic polynomial regression
  • evolution time scale
  • texture images


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