Partition-induced connections and operators for pattern analysis

Georgios K. Ouzounis, Michael H.F. Wilkinson

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
247 Downloads (Pure)

Abstract

In this paper we present a generalization on the notion of image connectivity similar to that modeled by second-generation connections. The connected operators based on this new type of connection make use of image partitions aided by mask images to extract path-wise connected regions that were previously treated as sets of singletons. This leads to a redistribution of image power which affects texture descriptors. These operators find applications in problems involving contraction-based connectivities, and we show how they can be used to counter the over-segmentation problem of connected filters. Despite restrictions which prevent extensions to gray-scale, we present a method for gray-scale spectral analysis of biomedical images characterized by filamentous details. Using connected pattern spectra as feature vectors to train a classifier we show that the new operators outperform the existing contraction-based ones and that the classification performance competes with, and in some cases outperforms methods based on the standard 4- or 8-connectivity. Finally, combining the two methods we enrich the texture description and increase the overall classification rate. (C) 2009 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)3193-3207
Number of pages15
JournalPattern recognition
Volume43
Issue number10
DOIs
Publication statusPublished - Oct-2010

Keywords

  • Image analysis
  • Mathematical morphology
  • Connected filters
  • Connectivity classes
  • Diatoms
  • CURVATURE SCALE SPACES
  • SHAPE REPRESENTATION
  • COMPLETE LATTICES
  • FILTERS
  • IMAGE
  • CLASSIFICATION
  • RECOGNITION
  • OPENINGS

Cite this