An efficient algorithm for point matching using hilbert scanning distance

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A fast and accurate similarity named Hilbert Scanning Distance(HSD) [9] has recently been presented for point matching. In this study, we improved an efficient algorithm of search strategy for HSD in the large search space. This search strategy is associated with two ideas: a relaxation greedy search, and an accelerating process using Monte Carlo sampling. The experimental results implicate that this improved algorithm is robust and efficient for point matching using HSD. It also makes a tradeoff between accuracy and speed under different requirements.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages873-876
Number of pages4
DOIs
Publication statusPublished - 2006 Dec 1
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period06/8/2006/8/24

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Tian, L., & Kamata, S. I. (2006). An efficient algorithm for point matching using hilbert scanning distance. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 873-876). [1699664] (Proceedings - International Conference on Pattern Recognition; Vol. 3). https://doi.org/10.1109/ICPR.2006.237