Please use this identifier to cite or link to this item:
Title: A saliency-weighted orthogonal regression-based similarity measure for entropic graphs
Authors: Ergün, Aslı
Ergün, Serkan
Ünlü, Mehmet Zübeyir
Güngör, Cengiz
Keywords: Entropic graphs
Image registration
Parameter search
Optimization techniques
Feature sets
Joint saliency map
Publisher: Springer
Abstract: Various measures are used to determine similarity ratios among images before and after image registration. Image registration methods are based on finding the translation, rotation, and scaling parameters that maximize the similarity between two images by taking advantage of the feature points and densities that are found. While the similarity criterion is calculated, it is possible and advantageous to use approximation methods on the graphs based on information theory. The current study proposes a new similarity measure based on saliency-weighted orthogonal regression derived from the weighted sums of the saliency map of the orthogonal regression residuals formed on the entropic graph. It is evaluated in terms of both quantitative and qualitative methods and compared with other graph-based similarity measures.
ISSN: 1863-1703
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File SizeFormat 
s11760-019-01483-8.pdf1.61 MBAdobe PDFView/Open
Show full item record

CORE Recommender

Page view(s)

checked on Apr 8, 2024


checked on Apr 8, 2024

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.