Elastic graph matching on gabor feature representation at low image resolution

Yasuomi Sato, Yasutaka Kuriya

研究成果: Conference contribution

抜粋

We progressively improve conventional elastic graph matching (EGM) algorithm. In the conventional EGM, each node of a model graph can difficultly detect its corresponding precise position for the most similar Gabor feature extraction on an input low-resolution image. Solving this problem and then finding such a position, we propose a method that the node is allowed to fit among pixels by interpolating aliased Gabor feature representation between the pixels, which is calculated with the others extracted at the neighbor pixels. The model graph can thereby move to the most likely and more precise positions on the input low-resolution image.

元の言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
ページ387-394
ページ数8
エディションPART 1
DOI
出版物ステータスPublished - 2012 10 25
イベント22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
継続期間: 2012 9 112012 9 14

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
7552 LNCS
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Other

Other22nd International Conference on Artificial Neural Networks, ICANN 2012
Switzerland
Lausanne
期間12/9/1112/9/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Sato, Y., & Kuriya, Y. (2012). Elastic graph matching on gabor feature representation at low image resolution. : Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings (PART 1 版, pp. 387-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 7552 LNCS, 番号 PART 1). https://doi.org/10.1007/978-3-642-33269-2_49