A saliency-weighted orthogonal regression-based similarity measure for entropic graphs
SIGNAL IMAGE AND VIDEO PROCESSING, vol.13, no.7, pp.1377-1385, 2019 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 13 Issue: 7
- Publication Date: 2019
- Doi Number: 10.1007/s11760-019-01483-8
- Journal Name: SIGNAL IMAGE AND VIDEO PROCESSING
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.1377-1385
- Keywords: Entropic graphs, Image registration, Parameter search, optimization technique, Feature sets, Joint saliency map, Orthogonal regression-based entropic graphs
- Dokuz Eylül University Affiliated: Yes
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.