Picture of neon light reading 'Open'

Discover open research at Strathprints as part of International Open Access Week!

23-29 October 2017 is International Open Access Week. The Strathprints institutional repository is a digital archive of Open Access research outputs, all produced by University of Strathclyde researchers.

Explore recent world leading Open Access research content this Open Access Week from across Strathclyde's many research active faculties: Engineering, Science, Humanities, Arts & Social Sciences and Strathclyde Business School.

Explore all Strathclyde Open Access research outputs...

Strathprints

Morphological granulometry for classification of evolving and ordered texture images.

Khatun, Mahmuda and Gray, Alison and Marshall, Stephen (2011) Morphological granulometry for classification of evolving and ordered texture images. In: 19th European Signal Processing Conference -EUSIPCO 2011, 2011-08-29 - 2011-09-02.

[img]
Preview
 PDF
1569424689.pdf - Final Published Version
Download (738kB) | Preview

Abstract

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.

Item type: Conference or Workshop Item (Paper)
ID code: 33201
Keywords: morphological granulometric moments, cubic polynomial regression, evolution time scale , texture images, Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Science > Mathematics and Statistics
Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
Depositing user: Pure Administrator
Date deposited: 13 Sep 2011 14:57
Last modified: 25 Sep 2017 17:31
URI: https://strathprints.strath.ac.uk/id/eprint/33201
Export data:
CORE (COnnecting REpositories)