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Proceedings Paper

Edge Linking by Ellipsoidal Clustering
Author(s): Visvanathan Ramesh; Robert M. Haralick
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Paper Abstract

This paper discusses a method for edge linking using an ellipsoidal clustering technique. The ellipsoidal clustering technique assumes that each data point is an ellipsoid with a mean and covariance matrix and generates a decision tree which partitions the sample ellipsoids into clusters. The problem of edge linking can be visualized as a clustering process. By assuming the properties of each edge pixel to be components of the data vector, pixels having similar properties are clustered together and pixels in the same cluster are linked together. The edge data is obtained using the facet model based edge detector and the calculation of the property vectors and the covariance matrices of the edge pixels is also computed from the facet edge detector output. The performance of the clustering algorithm is evaluated by computing the average clustering error and the relationships between the clustering threshold, the noise level and the clustering error are outlined.

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: 10.1117/12.969730
Show Author Affiliations
Visvanathan Ramesh, University of Washington (United States)
Robert M. Haralick, University of Washington (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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