Abstract

Point pattern matching is an essential step in many image processing applications. This letter investigates the spectral approaches of point pattern matching, and presents a spectral feature matching algorithm based on kernel partial least squares (KPLS). Given the feature points of two images, we define position similarity matrices for the reference and sensed images, and extract the pattern vectors from the matrices using KPLS, which indicate the geometric distribution and the inner relationships of the feature points. Feature points matching are done using the bipartite graph matching method. Experiments conducted on both synthetic and real-world data demonstrate the robustness and invariance of the algorithm.

© 2011 Chinese Optics Letters

Reflectance spectra reconstruction from trichromatic camera based on kernel partial least square method

Gensheng Xiao, Xiaoxia Wan, Lixia Wang, and Shiwei Liu
Opt. Express 27(24) 34921-34936 (2019)

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Haiyan Cen, Yidan Bao, and Yong He
Appl. Opt. 45(29) 7679-7683 (2006)

Thermal-to-visible face recognition using partial least squares

Shuowen Hu, Jonghyun Choi, Alex L. Chan, and William Robson Schwartz
J. Opt. Soc. Am. A 32(3) 431-442 (2015)

Single-frame-based column fixed-pattern noise correction in an uncooled infrared imaging system based on weighted least squares

Fangzhou Li, Yaohong Zhao, and Wei Xiang
Appl. Opt. 58(33) 9141-9153 (2019)

Weighted least-squares phase-unwrapping algorithm based on the orientation coherence for discontinuous optical phase patterns

Biyuan Li, Chen Tang, Qiuling Zhou, and Zhenkun Lei
Appl. Opt. 58(2) 219-226 (2019)