Automatic Image-Map Alignment Using Edge-Based Code Mutual Information and 3-D Hilbert Scan

Research output: Contribution to journalArticle

Abstract

This study presents a new algorithm for automatic image-map alignment problem using a new similarity measure named Edge-Based Code Mutual Information (EBCMI) and 3-D Hilbert scan. In general, each image-map pair can be viewed as two special multimodal images, however, are very different in their representations such as the intensity. Therefore, the normal Mutual Information (MI) using the intensity in traditional alignment method may result in misalignment. To solve the problem, codes based on the edges of the image-map pairs are constructed and Mutual Information of the codes is computed as the similarity measure for the alignment in our method. Since Edge-Based Code (EBC) is robust to the differences between the image-map pairs in their representations, EBCMI also can overcome the differences. On the other hand, the 3-D search space in alignment can be converted to a 1-D search space sequence by 3-D Hilbert Scan and a new search strategy is proposed on the 1-D search space sequence. The experimental results show that the proposed EBCMI performed better than the normal MI and some other similarity measures and the proposed search strategy gives flexibility between efficiency and accuracy for automatic image-map alignment task.

Original languageEnglish
Pages (from-to)223-230
Number of pages8
JournalJournal of the Institute of Image Electronics Engineers of Japan
Volume37
Issue number3
DOIs
Publication statusPublished - 2008

Keywords

  • 3-D Hilbert scan
  • Automatic image-map alignment
  • Edge-Based Code
  • Mutual Information
  • Search strategy

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering

Cite this

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title = "Automatic Image-Map Alignment Using Edge-Based Code Mutual Information and 3-D Hilbert Scan",
abstract = "This study presents a new algorithm for automatic image-map alignment problem using a new similarity measure named Edge-Based Code Mutual Information (EBCMI) and 3-D Hilbert scan. In general, each image-map pair can be viewed as two special multimodal images, however, are very different in their representations such as the intensity. Therefore, the normal Mutual Information (MI) using the intensity in traditional alignment method may result in misalignment. To solve the problem, codes based on the edges of the image-map pairs are constructed and Mutual Information of the codes is computed as the similarity measure for the alignment in our method. Since Edge-Based Code (EBC) is robust to the differences between the image-map pairs in their representations, EBCMI also can overcome the differences. On the other hand, the 3-D search space in alignment can be converted to a 1-D search space sequence by 3-D Hilbert Scan and a new search strategy is proposed on the 1-D search space sequence. The experimental results show that the proposed EBCMI performed better than the normal MI and some other similarity measures and the proposed search strategy gives flexibility between efficiency and accuracy for automatic image-map alignment task.",
keywords = "3-D Hilbert scan, Automatic image-map alignment, Edge-Based Code, Mutual Information, Search strategy",
author = "Seiichiro Kamata and Li Tian",
year = "2008",
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language = "English",
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AB - This study presents a new algorithm for automatic image-map alignment problem using a new similarity measure named Edge-Based Code Mutual Information (EBCMI) and 3-D Hilbert scan. In general, each image-map pair can be viewed as two special multimodal images, however, are very different in their representations such as the intensity. Therefore, the normal Mutual Information (MI) using the intensity in traditional alignment method may result in misalignment. To solve the problem, codes based on the edges of the image-map pairs are constructed and Mutual Information of the codes is computed as the similarity measure for the alignment in our method. Since Edge-Based Code (EBC) is robust to the differences between the image-map pairs in their representations, EBCMI also can overcome the differences. On the other hand, the 3-D search space in alignment can be converted to a 1-D search space sequence by 3-D Hilbert Scan and a new search strategy is proposed on the 1-D search space sequence. The experimental results show that the proposed EBCMI performed better than the normal MI and some other similarity measures and the proposed search strategy gives flexibility between efficiency and accuracy for automatic image-map alignment task.

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