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Proceedings Paper

Image Segmentation By Background Extraction Refinements
Author(s): Arturo A. Rodriguez; O. Robert Mitchell
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Paper Abstract

A segmentation method that refines thresholds that extract the local background is introduced. During the first phase, the method decomposes the image into rectangular regions and measures the left and right standard deviations and graytone mean of each region. Background-homogeneous regions are detected by using criteria based on statistical theory. The background of a homogeneous region is extracted by computing the left and right shoulder thresholds of its graytone distribution from the measured standard deviations and graytone mean of the region. The corresponding thresholds for a non-homogeneous region are computed from estimates of the standard deviations and mean of its background. Pixels are then classified as darker than background, background, or brighter than background. The second phase of the method focuses background extraction refinements in non-homogeneous regions. The statistics of the background classified pixels in each non-homogeneous region are measured. If the set of background classified pixels in a region exhibits homogeneity, background extraction is ameliorated by computing thresholds from the new background statistics. The homogeneity criteria is tightened as the number of background classified pixels in a region decreases. If the set of background classified pixels in a region is non-homogeneous it suggests pixel misclassifications. The final background statistics of the region are then estimated by comparing background statistics measured in two successive trials, and from whether the set of pixels is left or right non-homogeneous, or both. This heuristical approach was implemented by studying non-homogeneous regions in a set of industrial images of moderate complexity. Rules that predict the final background extraction were derived by observing the behavior of successive background statistical measurements in the regions under the presence of dark and/or bright object pixels. Results indicate a significant reduction of background clutter in the industrial scenes. Good results have also been obtained in outdoor scenes of moderate complexity.

Paper Details

Date Published: 1 March 1990
PDF: 13 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969728
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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