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Proceedings Paper

A Class Of Iterative Thresholding Algorithms For Real-Time Image Segmentation
Author(s): M. H. Hassan
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Paper Abstract

Thresholding algorithms are developed for segmenting gray-level images under nonuniform illumination. The algorithms are based on learning models generated from recursive digital filters which yield to continuously varying threshold tracking functions. A real-time region growing algorithm, which locates the objects in the image while thresholding, is developed and implemented. The algorithms work in a raster-scan format, thus making them attractive for real-time image segmentation in situations requiring fast data throughput such as robot vision and character recognition.

Paper Details

Date Published: 27 March 1989
PDF: 12 pages
Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960272
Show Author Affiliations
M. H. Hassan, Lawrence Technological University (United States)

Published in SPIE Proceedings Vol. 1002:
Intelligent Robots and Computer Vision VII
David P. Casasent, Editor(s)

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