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- W4367011761 abstract "Image registration uses attributes or similarity measures to align images taken at various times or from different points of view. In this work, we provide an image registration parameter estimation approach based on structural features. The scale invariant feature transform (SIFT) retrieves structural features such as edge, corner, and so on. To choose the most intrinsic element from the retrieved feature vector, the arithmetic optimisation algorithm (AOA) is applied. On image databases that are publicly accessible such as standard benchmark images, synthetic aperture radar (SAR) and medical images, the proposed technique’s effectiveness is compared to different earlier state-of-the-art methodologies. According to the results of this experiment, this strategy can reduce the mean square error of inaccurate image registration parameter predictions." @default.
- W4367011761 created "2023-04-27" @default.
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- W4367011761 date "2023-01-01" @default.
- W4367011761 modified "2023-09-25" @default.
- W4367011761 title "Estimation of Different Transformation Parameters Based on Optimised Scale Invariant Feature Transform for Image Registration" @default.
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- W4367011761 doi "https://doi.org/10.1007/978-981-19-5191-6_52" @default.
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