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Proceedings Paper

Line Thinning via Merge-Split in Run-Length Sequences of Line Cross Sections
Author(s): Gongzhu Hu; Ze-Nian Li
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Paper Abstract

Thinning is an image processing procedure that extracts the medial axes, or skeletons, of objects in a binary image. Because of the iterative pixel-removing strategy used, most existing thinning algorithms are either inefficient (sequential algorithms) or need special hardware (parallel algorithms). Furthermore, for line-shaped objects, the line intersections produced by these algorithms tend to be elongated. A new line thinning and intersection detection approach is presented in this paper that deals with images in which objects are lines (curves). It uses run-length representation for the lines in the image. A histogram of run length is consulted to identify runs that correspond to line cross sections. The mid-points of the selected runs are used to form the skeletons. Line intersections are detected at locations where the sequences of runs merge or split. This approach is non-iterative with a time complexity linear to the size of the image.

Paper Details

Date Published: 1 March 1990
PDF: 11 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969732
Show Author Affiliations
Gongzhu Hu, Central Michigan University (United States)
Ze-Nian Li, Simon Fraser University (Canada)

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

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