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Proceedings Paper

Image segmentation via piecewise constant regression
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Paper Abstract

We introduce a novel unsupervised image segmentation technique that is based on piecewise constant (PICO) regression. Given an input image, a PICO output image for a specified feature size (scale) is computed via nonlinear regression. The regression effectively provides the constant region segmentation of the input image that has a minimum deviation from the input image. PICO regression-based segmentation avoids the problems of region merging, poor localization, region boundary ambiguity, and region fragmentation. Additionally, our segmentation method is particularly well-suited for corrupted (noisy) input data. An application to segmentation and classification of remotely sensed imagery is provided.

Paper Details

Date Published: 16 September 1994
PDF: 12 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185955
Show Author Affiliations
Scott Thomas Acton, Oklahom State Univ. (United States)
Alan Conrad Bovik, Univ. of Texas/Austin (United States)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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