Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph matching for shape retrieval
Proceedings of the 1998 conference on Advances in neural information processing systems II
Fuzzy Relational Distance for Large-Scale Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Light-years from Lena: video and image libraries of the future
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Image Indexing using Composite Color and Shape Invariant Features
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Relational Histograms for Shape Indexing
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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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.