Edge Linking by Ellipsoidal Clustering
Visvanathan Ramesh, Robert M. Haralick · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
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.