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Proceedings Paper

Cortex transform and its application for supervised texture classification of digital images
Author(s): M. K. Bashar; Noboru Ohnishi; R. K. Shevgaonkar
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Paper Abstract

This paper proposes a localized multi-channel filtering approach of image texture analysis based on the cortical behavior of Human Visual System (HVS). In our efforts, 2D Gaussian function, called Cortex Filter, in the frequency domain is used to model the band pass nature of simple cells in HVS. A block-based iterative method is addressed. In each pass, a square block of data is captured and cortex filters at various directions and radial bands are applied to filter out the available texture information in that block. Such decomposition results in a set of band pass images from a single input image and we call it Cortex Transform (CT). We use filter responses in each pass to compute the representative texture features i.e., the average filtered energies. The procedure is repeated for the subsequent blocks of data until the whole image is scanned. Various energy values calculated above are stored into different arrays or files and are regarded as feature images. Thus the obtained feature images are integrated with minimum distance classifier for supervised texture classification. We demonstrated the algorithm with various real world and synthetic images from various sources. Confusion matrix analysis shows a high average overall classification accuracy (97.01%) of our CT based approach in comparison with that (71.27%) of the popular gray level co-occurrence matrix (GLCM) approach.

Paper Details

Date Published: 11 February 2002
PDF: 12 pages
Proc. SPIE 4567, Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II, (11 February 2002); doi: 10.1117/12.455251
Show Author Affiliations
M. K. Bashar, Nagoya Univ. (Japan)
Noboru Ohnishi, RIKEN-The Institute of Physical and Chemical Research (Japan)
R. K. Shevgaonkar, Indian Institute of Technology (India)

Published in SPIE Proceedings Vol. 4567:
Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II
Kevin G. Harding; John W. V. Miller, Editor(s)

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