Improved polar complex exponential transform for robust local image description

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

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

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.

源语言英语
文章编号109786
期刊Pattern Recognition
143
DOI
出版状态已出版 - 11月 2023

指纹

探究 'Improved polar complex exponential transform for robust local image description' 的科研主题。它们共同构成独一无二的指纹。

引用此