Improved polar complex exponential transform for robust local image description

Zhanlong Yang, Linzhi Yang, Geng Chen, Pew Thian Yap

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Image description via robust local descriptors plays a vital role in a large number of image representation and matching applications. In this paper, we propose a novel distinctive local image descriptor that is based on the phase and amplitude information of Polar Complex Exponential Transform (PCET). The proposed descriptor, called IPCET (Improved PCET), is robust to the common photometric transformations (e.g., illumination, noise, JPEG compression, and blur) and geometric transformations (e.g., scaling, rotation, translation, and significant affine distortion). We perform extensive experiments to compare our IPCET descriptor with six most cutting-edge region descriptors (i.e., SIFT, Zernike Moment, GLOH, PCA-SIFT, SURF, and ORB). Experimental results demonstrate that our IPCET descriptor outperforms cutting-edge moment-based descriptors.

Original languageEnglish
Article number109786
JournalPattern Recognition
Volume143
DOIs
StatePublished - Nov 2023

Keywords

  • Image description
  • Local image descriptor
  • Phase information
  • Polar complex exponential transform

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