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Proceedings Paper

Rapid Adaptive Segmentation By Estimation And Reassignment Of Uncertainty Pixels
Author(s): Arturo A. Rodriguez; O. Robert Mitchell
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Paper Abstract

A segmentation approach that uses successive cost processing is introduced. By processing graytone information the background is extracted adaptively throughout the image and pixels are classified into uncertainty and brightness relative to local background. The uncertainty pixels correspond to an estimate of the overlap between the graytone distributions of the background and dark objects, and the background and bright objects. Uncertainty pixels identify image regions requiring further processing with local and spatial information rather than merely graytone information. One rapid method resolves uncertainty by selecting thresholds at the maxima of local normalized edge magnitude histograms. The statistical means of floating histograms and of the estimated local background are used to control the search for thresholds in the local normalized edge magnitude histograms. Floating histograms retain the local brightness relationship between non-background pixels and the background. This method can distinguish between seven or less distributions locally by determining additional thresholds in the normalized edge magnitude histograms of dark pixels and bright pixels. The graytone image is then mapped into a multiple-label image corresponding to object brightness relative to local background. Another method erodes boundaries of uncertainty regions with a non-maxima edge magnitude suppression technique that insures consistent gradient direction among adjacent edgels. Gradient direction space is partitioned into certainty and uncertainty arc zones. A certainty arc zone directs an edgel during non-maxima suppression to the 8-neighbor it bounds; an uncertainty zone to the two 8-neighbors that bound the arc zone. Good quality and rapid segmentations have been obtained in industrial scenes of modest complexity. Background-object and interior object boundaries are satisfactorily outlined and the enclosed regions are represented with the proper relative brightness label.

Paper Details

Date Published: 1 March 1990
PDF: 12 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969727
Show Author Affiliations
Arturo A. Rodriguez, IBM (United States)
O. Robert Mitchell, University of Texas (United States)

Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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