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Proceedings Paper

A Filter Design Approach To Textured Image Segmentation
Author(s): Parvez K. Bashir
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Paper Abstract

A scheme is presented for segmenting textured images using a filter design approach. The discrete Fourier transform (DFT) of each texture in the training set is computed. Using the DFT information, an empirical method for evaluating the texel size (in pixels) for the training set of textures is given. A separable filter template of a particular texel size is designed for each texture based on the DFT. Each textured image is then convolved with these sets of filter templates. A training feature vector is stored in the classifier for each texture by summing the outputs of the filtered images. For texture classification or segmentation, a texture mosaic consisting of one or more textures in the data set is convolved with the same set of filter templates applied in the training procedure. Each filtered image output is summed within image blocks of a particular texel size. A feature vector is computed for each block and fed into a minimum distance classifier. Classification accuracies of more than 90% are achieved using a set of four textures from Brodatzl album of textures.

Paper Details

Date Published: 26 March 1986
PDF: 7 pages
Proc. SPIE 0635, Applications of Artificial Intelligence III, (26 March 1986); doi: 10.1117/12.964149
Show Author Affiliations
Parvez K. Bashir, University of Hawaii (United States)


Published in SPIE Proceedings Vol. 0635:
Applications of Artificial Intelligence III
John F. Gilmore, Editor(s)

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