Moment Symmetry: A novel method for interest point detection to match blurred and non-blurred images

Tong Qiang, Terumasa Aoki

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

3 Citations (Scopus)

Abstract

To match a pair of images, currently lots of local features can be used. Many of them show good performances for image changes such as rotation, resizing, and occlusion etc. However, matching between a blurred (especially, motion-blurred) image and a non-blurred image is still a challenging task although it is required for many image/video applications. Local feature matching is usually composed of three steps: interest point detection, feature description for each interest point, and matching these features. Unfortunately interest point detection is not robust for strong blur in most existing methods. In this paper, we present Moment Symmetry (MS) to solve this problem. MS is a new concept of symmetry against traditional symmetry. By using MS, we can extract the same interest points from a blurred image and a non-blurred image in a simple way. Experimental results show MS outperforms existing methods as a novel interest point detector.

Original languageEnglish
Title of host publication2016 International Conference on Image, Vision and Computing, ICIVC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages26-30
Number of pages5
ISBN (Electronic)9781509037544
DOIs
Publication statusPublished - 2016 Sep 19
Event2016 International Conference on Image, Vision and Computing, ICIVC 2016 - Portsmouth, United Kingdom
Duration: 2016 Aug 32016 Aug 5

Publication series

Name2016 International Conference on Image, Vision and Computing, ICIVC 2016

Other

Other2016 International Conference on Image, Vision and Computing, ICIVC 2016
Country/TerritoryUnited Kingdom
CityPortsmouth
Period16/8/316/8/5

Keywords

  • blur
  • detector
  • local feature
  • moments
  • symmetry

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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