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Proceedings Paper

Informed edge linking using a directional potential function
Author(s): Victoria Riordan; Quiming Zhu
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Paper Abstract

Low level edge detection operators usually do not generate contiguous edges, leaving objects in images with discontinuous borders. This, coupled with inherent signal noises, makes it difficult to identify objects in images. Here we describe a new algorithm that connects disjoint edge pixels to form continual object boundaries. We model the edge images as potential fields deployed with energies at the edge pixel positions all over the images. Pixels at the edge disjoint positions are charged by the combining forces of these edge pixels in proportion to the relative distances and directions of these pixels. An intrinsic part of the process is the identification of terminal edge pixels (TEP), accompanying with a classification of edge pixels in terms of the pixel connection patterns, to provide critical information for possible connectivity of edge segments. The algorithm applies a potential evaluation function to measure the likelihood of edge linking in certain directions for given TEPs. To reduce the computational overhead and improve the efficiency of the algorithm, an informed search method is used to locate significant edge pixels that present the most strong linking forces to a given TEP. The potential value for the TEP is calculated with respect to the edge directions dominated by the linking forces. When the potential value exceeds a given threshold in a direction, an extension is made at the TEP position in that direction. The process iterates until desired results are attained, using a global edge pattern evaluation scheme.

Paper Details

Date Published: 20 August 1993
PDF: 12 pages
Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150155
Show Author Affiliations
Victoria Riordan, Univ. of Nebraska/Omaha (United States)
Quiming Zhu, Univ. of Nebraska/Omaha (United States)

Published in SPIE Proceedings Vol. 2055:
Intelligent Robots and Computer Vision XII: Algorithms and Techniques
David P. Casasent, Editor(s)

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