Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9526
Title: A saliency-weighted orthogonal regression-based similarity measure for entropic graphs
Authors: Ergün, Aslı
Ergün, Serkan
Ünlü, Mehmet Zübeyir
Güngör, Cengiz
Ünlü, Mehmet Zübeyir
Izmir Institute of Technology. Electronics and Communication Engineering
Keywords: Entropic graphs
Image registration
Parameter search
optimization technique
Feature sets
Joint saliency map
Orthogonal regression-based entropic graphs
Issue Date: 2019
Publisher: Springer Verlag
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.
Description: Unlu, Mehmet Zubeyir/0000-0003-1605-0160; Ergun, Asli/0000-0003-0476-0069
WOS: 000490956300015
URI: https://doi.org/10.1007/s11760-019-01483-8
https://hdl.handle.net/11147/9526
ISSN: 1863-1703
1863-1711
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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