Water reflection recognition based on motion blur invariant moments in curvelet space

Sheng Hua Zhong, Yan Liu, Yang Liu, Chang Sheng Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

Water reflection, a typical imperfect reflection symmetry problem, plays an important role in image content analysis. Existing techniques of symmetry recognition, however, cannot recognize water reflection images correctly because of the complex and various distortions caused by the water wave. Hence, we propose a novel water reflection recognition technique to solve the problem. First, we construct a novel feature space composed of motion blur invariant moments in low-frequency curvelet space and of curvelet coefficients in high-frequency curvelet space. Second, we propose an efficient algorithm including two sub-algorithms: low-frequency reflection cost minimization and high-frequency curvelet coefficients discrimination to classify water reflection images and to determine the reflection axis. Through experimenting on authentic images in a series of tasks, the proposed techniques prove effective and reliable in classifying water reflection images and detecting the reflection axis, as well as in retrieving images with water reflection.
Original languageEnglish
Article number6553236
Pages (from-to)4301-4313
Number of pages13
JournalIEEE Transactions on Image Processing
Volume22
Issue number11
DOIs
Publication statusPublished - 30 Sep 2013

Keywords

  • imperfect symmetry
  • invariant moments
  • motion blur
  • reflection axis detection
  • Water reflection

ASJC Scopus subject areas

  • Software
  • Medicine(all)
  • Computer Graphics and Computer-Aided Design

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