Modeling of evolving textures using granulometries

Alison Gray, Stephen Marshall, Jennifer McKenzie

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)
104 Downloads (Pure)


This chapter describes a statistical approach to classification of dynamic
texture images, called parallel evolution functions (PEFs). Traditional classification
methods predict texture class membership using comparisons with
a finite set of predefined texture classes and identify the closest class. However,
where texture images arise from a dynamic texture evolving over time,
estimation of a time state in a continuous evolutionary process is required
instead. The PEF approach does this using regression modeling techniques
to predict time state. It is a flexible approach which may be based on any
suitable image features. Many textures are well suited to a morphological
analysis and the PEF approach uses image texture features derived from a
granulometric analysis of the image.
The method is illustrated using both simulated images of Boolean processes
and real images of corrosion. The PEF approach has particular advantages
for training sets containing limited numbers of observations, which
is the case in many real world industrial inspection scenarios and for which
other methods can fail or perform badly.
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Original languageEnglish
Title of host publicationAdvances in Nonlinear Signal and Image Processing
EditorsStephen Marshall Giovanni L. Sicuranza
Place of PublicationCairo, Egypt
Number of pages33
Publication statusPublished - 2006

Publication series

NameEURASIP Book Series on Signal Processing and Communications
PublisherHindawi Publishing Corporation


  • parallel evolution functions

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