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Partition-induced connections and operators for pattern analysis
- Source :
-
Pattern Recognition . Oct2010, Vol. 43 Issue 10, p3193-3207. 15p. - Publication Year :
- 2010
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 43
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 51810353
- Full Text :
- https://doi.org/10.1016/j.patcog.2009.10.002