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Proceedings Paper

Rotation invariant features for wear particle classification
Author(s): Hamzah Arof; Farzin Deravi
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Paper Abstract

This paper investigates the ability of a set of rotation invariant features to classify images of wear particles found in used lubricating oil of machinery. The rotation invariant attribute of the features is derived from the property of the magnitudes of Fourier transform coefficients that do not change with spatial shift of the input elements. By analyzing individual circular neighborhoods centered at every pixel in an image, local and global texture characteristics of an image can be described. A number of input sequences are formed by the intensities of pixels on concentric rings of various radii measured from the center of each neighborhood. Fourier transforming the sequences would generate coefficients whose magnitudes are invariant to rotation. Rotation invariant features extracted from these coefficients were utilized to classify wear particle images that were obtained from a number of different particles captured at different orientations. In an experiment involving images of 6 classes, the circular neighborhood features obtained a 91% recognition rate which compares favorably to a 76% rate achieved by features of a 6 by 6 co-occurrence matrix.

Paper Details

Date Published: 18 September 1997
PDF: 5 pages
Proc. SPIE 3205, Machine Vision Applications, Architectures, and Systems Integration VI, (18 September 1997); doi: 10.1117/12.285590
Show Author Affiliations
Hamzah Arof, Univ. of Wales Swansea (United Kingdom)
Farzin Deravi, Univ. of Wales Swansea (United Kingdom)


Published in SPIE Proceedings Vol. 3205:
Machine Vision Applications, Architectures, and Systems Integration VI

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