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Author Huet, B. ♦ Hancock, E.R.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1998
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Large-scale systems ♦ Object recognition ♦ Libraries ♦ Noise robustness ♦ Kernel ♦ Image representation ♦ Histograms ♦ Computer science ♦ Bayesian methods ♦ Geometry
Abstract This paper presents a new similarity measure for object recognition from large libraries of line-patterns. The measure draws its inspiration from both the Hausdorff distance and a recently reported Bayesian consistency measure that has been successfully used for graph-based correspondence matching. The measure uses robust error-kernels to gauge the similarity of pair-wise attribute relations defined on the edges of nearest neighbour graphs. We use the similarity measure in a recognition experiment which involves a library of over 1000 line-patterns. A sensitivity study reveals that the method is capable of delivering a recognition accuracy of 98%. A comparative study reveals that the method is most effective when a Gaussian kernel or Huber's robust kernel is used to weight the attribute relations. Moreover, the method consistently outperforms Rucklidge's median Hausdorff distance (1995).
Description Author affiliation: Dept. of Comput. Sci., York Univ., UK (Huet, B.)
ISBN 0818684976
ISSN 10636919
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1998-06-25
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 388.77 kB
Page Count 6
Starting Page 138
Ending Page 143


Source: IEEE Xplore Digital Library