Abstract
This paper provides a detailed sensitivity analysis for the problem of recognising line patterns from large structural libraries. The analysis focuses on the characterization of two different recognition strategies. The first is histogram-based while the second uses feature-sets. In the former case comparison is based on the Bhattacharyya distance between histograms, while in the latter case the feature-sets are compared using a probabilistic variant of the Hausdorff distance. We study the two algorithms under line-dropout, line fragmentation, line addition and line end-point position errors. The analysis reveals that while the histogram-based method is most sensitive to the addition of line segments and end-point position errors, the set-based method is most sensitive to line dropout.
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© 1999 Springer-Verlag Berlin Heidelberg
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Huet, B., Hancock, E.R. (1999). Structural Sensitivity for Large-Scale Line-Pattern Recognition. In: Huijsmans, D.P., Smeulders, A.W.M. (eds) Visual Information and Information Systems. VISUAL 1999. Lecture Notes in Computer Science, vol 1614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48762-X_88
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DOI: https://doi.org/10.1007/3-540-48762-X_88
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