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Fitting straight lines to point patterns

Authors
Journal
Pattern Recognition
0031-3203
Publisher
Elsevier
Publication Date
Volume
17
Issue
5
Identifiers
DOI: 10.1016/0031-3203(84)90045-1
Keywords
  • Hierarchical Clustering
  • Constrained Clustering
  • Principal Components Analysis
  • Karhunen-Loève Expansion
  • Variance
  • Unsupervised Classification
Disciplines
  • Computer Science

Abstract

Abstract In many types of point patterns, linear features are of greatest interest. A very general algorithm is presented here which determines non-overlapping clusters of points which have large linearity. Given a set of points, the algorithm successively merges pairs of clusters or of points, encompassing in the merging criterion both contiguity and linearity. The algorithm is a generalization of the widely-used Ward's minimum variance hierarchical clustering method. The application of this algorithm is illustrated using examples from the literature in biometrics and in character recognition.

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