A Fast and Accurate Point Pattern Matching Algorithm Based on Multi-Hilbert Scans

Jegoon Ryu*, Sei ichiro Kamata

*この研究の対応する著者

研究成果: Conference contribution

抄録

This paper proposes a novel distance measurement using multi-Hilbert scans for matching point patterns on images. A modified Hausdorff distance has been widely used for point pattern matching, recognition tasks, and evaluation of medical image segmentation. However, the computation cost increases sharply with the number of feature points or the increase of data sets. Multi-Hilbert Scanning Distance (MHSD) based on sets of one-dimensional points using Hilbert scans is introduced to overcome this problem. MHSD consists of a combination of four directional Hilbert scans and diagonally shifted Hilbert scans. The proposed method was tested on vehicle images and compared with Hausdorff distance, partial Hausdorff distance, and modified Hausdorff distance. Experimental results show that the proposed method outperforms the compared methods.

本文言語English
ホスト出版物のタイトルPattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers
編集者Christian Wallraven, Qingshan Liu, Hajime Nagahara
出版社Springer Science and Business Media Deutschland GmbH
ページ562-574
ページ数13
ISBN(印刷版)9783031024436
DOI
出版ステータスPublished - 2022
イベント6th Asian Conference on Pattern Recognition, ACPR 2021 - Virtual, Online
継続期間: 2021 11月 92021 11月 12

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)
13189 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference6th Asian Conference on Pattern Recognition, ACPR 2021
CityVirtual, Online
Period21/11/921/11/12

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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