Comparative study of moment based parameterization for morphological texture description
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Date
2012
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Academic Press inc Elsevier Science
Abstract
The two principal morphological texture descriptors, granulometry and morphological covariance, rely on the common principle of successive filtering of an image using a variety of structuring elements, from which feature vectors are subsequently computed. A crucial stage of their computation is the numerical characterization or parameterization of each of the filtered images. In this regard, the zero-th statistical moment is the traditional measure, while the use of higher order moments has also been reported. In this paper, we present the results of a comparative study, concentrating on the potential of various statistical moments for the task of parameterization, while additionally investigating the contribution of Fourier transform moments. The experiments are conducted with focus on texture description effectiveness and on noise robustness, using publicly available texture collections: Outex, CUReT and KTH-TIPS2b, where it is shown that the combination of moments leads to superior classification performance even at high noise levels. (c) 2012 Elsevier Inc. All rights reserved.
Description
Aptoula, Erchan/0000-0001-6168-2883
Keywords
Texture analysis, Morphological Covariance, Granulometry, Parameterization, Statistical moments, Moment invariants, Fourier transform, Noise robustness
Turkish CoHE Thesis Center URL
Citation
8
WoS Q
Q2
Scopus Q
Q2
Source
Volume
23
Issue
8
Start Page
1213
End Page
1224