A saliency-weighted orthogonal regression-based similarity measure for entropic graphs
| Autor: | Asli Ergun, Cengiz Güngör, Mehmet Zubeyir Unlu, Serkan Ergun |
|---|---|
| Přispěvatelé: | Ege Üniversitesi |
| Rok vydání: | 2019 |
| Předmět: |
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image registration 02 engineering and technology Similarity measure Parameter search Information theory Translation (geometry) Entropic graphs optimization technique Similarity (network science) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Electrical and Electronic Engineering Total least squares Feature sets Joint saliency map business.industry 020206 networking & telecommunications Pattern recognition Orthogonal regression-based entropic graphs Computer Science::Computer Vision and Pattern Recognition Signal Processing 020201 artificial intelligence & image processing Artificial intelligence business Rotation (mathematics) |
| Zdroj: | Signal, Image and Video Processing. 13:1377-1385 |
| ISSN: | 1863-1711 1863-1703 |
| DOI: | 10.1007/s11760-019-01483-8 |
| Popis: | Unlu, Mehmet Zubeyir/0000-0003-1605-0160; Ergun, Asli/0000-0003-0476-0069 WOS: 000490956300015 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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