Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 7,
  • Issue 11,
  • pp. 996-1000
  • (2009)

Image registration based on matrix perturbation analysis using spectral graph

Not Accessible

Your library or personal account may give you access

Abstract

We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral graph. The contribution may be divided into three parts. Firstly, the perturbation matrix is obtained by perturbing the matrix of graph model. Secondly, an orthogonal matrix is obtained based on an optimal parameter, which can better capture correspondence features. Thirdly, the optimal matching matrix is proposed by adjusting signs of orthogonal matrix for image registration. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method.

© 2009 Chinese Optics Letters

PDF Article
More Like This
Block-based spectral image reconstruction for compressive spectral imaging using smoothness on graphs

Juan F. Florez-Ospina, Abdullah K. M. Alrushud, Daniel L. Lau, and Gonzalo R. Arce
Opt. Express 30(5) 7187-7209 (2022)

Analysis of macular OCT images using deformable registration

Min Chen, Andrew Lang, Howard S. Ying, Peter A. Calabresi, Jerry L. Prince, and Aaron Carass
Biomed. Opt. Express 5(7) 2196-2214 (2014)

Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights

Meng Gan, Cong Wang, Ting Yang, Na Yang, Miao Zhang, Wu Yuan, Xingde Li, and Lirong Wang
Biomed. Opt. Express 9(9) 4481-4495 (2018)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.