In the domain of digital processing of textures orthogonal transforms are applicable to the characterization of structural properties and to texture discrimination and synthesis. Two groups of approaches based on orthogonal transforms, namely power spectral methods and methods using correlation masks, are considered and compared. The latter propose to match orthogonal basis vectors with the image structure. The degree of match expresses itself in the variance found at the output of the correlation masks for a limited image region. It is one of the aims of this paper to show the close correspondence between the two groups of methods. Common aspects comprise the use of orthogonal transforms, of windows of certain sizes and of quadrature and averaging in the process of feature extraction. The main difference with respect to attainable resolution and representation of structural detail is due to the recommended dimensions of the windows which has consequences for the interpretation of the features as well as for the domain of applicability. The power spectral methods produce features which are appropriate to characterize textures which humans tend to qualify as periodic, striped, grainy etc. The correlation methods are not tuned to extract all of these features but, due to the smaller primary window size, are able to find texture boundaries. The good classification results obtained with the use of the correlation masks suggests, because of the close relationship between the two concepts, a reappraisal of the suitability of spectral methods for texture analysis problems. On the basis of the comparison it is tried to further a deeper understanding of spectral analysis and to show how to better apply it for texture classification.

Date Published: 21 April 1986
PDF: 7 pages
Proc. SPIE 0596, Architectures and Algorithms for Digital Image Processing III, (21 April 1986); doi: 10.1117/12.952295

Published in SPIE Proceedings Vol. 0596:
Architectures and Algorithms for Digital Image Processing III
Francis J. Corbett; Howard Jay Siegel; Michael J. Duff, Editor(s)