A local descriptor for high-speed and high-performance pictogram matching

Terumasa Aoki, Kurumi Kaminishi

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

Abstract

Pictogram, a simple picture-based symbol, is widely used to indicate important facilities (such as 'rest room') or important rules (such as 'no smoking'). If a practicable pictogram matching technology is available, it must be useful for many applications. Unfortunately, as far as the authors know, there are no practicable methods which satisfy both high-speed (real-time) processing and high performance (high matching ratio). In this paper, we present a novel local descriptor for high-speed and high-performance pictogram matching under a variety of photographing conditions. The proposed method consists of three modules: projection-invariant values called CRN (Cross Ratio Number), shape description scheme for pictograms, and matching acceleration method based on feature vector classification by using the relationship between contour and convex hull. The experiments show our method outperforms the state-of-the-art methods in terms of matching ratio and computation cost.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages1062-1066
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 2018 Feb 20
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 2017 Sep 172017 Sep 20

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period17/9/1717/9/20

Keywords

  • Image matching
  • Local feature
  • Pictogram
  • Shape descriptor

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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