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Proceedings Paper

A Connectionist Architecture For Computing Textural Segmentation
Author(s): Edmond Mesrobian; Josef Skrzypek
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Paper Abstract

This project examines some parallel architectures designed for image processing, and then addresses their applicability to the problem of image segmentation by texture analysis. Using this information, and research into the structure of the human visual system, an architecture for textural segmentation is proposed. The underlying premise is that textural segmentation can be achieved by recognizing local differences in texture elements (texels). This approach differs from most of the previous work where the differences in global, second-order statistics of the image points are used as the basis for segmentation. A realistic implementation of this approach requires a parallel computing architecture which consists of a hierarchy of functionally different nodes. First, simple features are extracted from the image. Second, these simple features are linked together to form more complex texels. Finally, local and more global differences in texels or their organization are enhanced and linked into boundaries.

Paper Details

Date Published: 6 June 1987
PDF: 9 pages
Proc. SPIE 0758, Image Understanding and the Man-Machine Interface, (6 June 1987); doi: 10.1117/12.940077
Show Author Affiliations
Edmond Mesrobian, University of California at Los Angeles (United States)
Josef Skrzypek, University of California at Los Angeles (United States)

Published in SPIE Proceedings Vol. 0758:
Image Understanding and the Man-Machine Interface
Eamon B. Barrett; James J. Pearson, Editor(s)

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