Abstract
The estimation of parametric global motion has had a significant attention during the last two decades, but despite the great efforts invested, there are still open issues. One of the most important ones is related to the ability to recover large deformation between images in the presence of illumination changes while kipping accurate estimates. In this paper, a Generalized least squared-based motion estimator is used in combination with a dynamic image model where the illumination factors are functions of the localization (x,y) instead of constants, allowing for a more general and accurate image model. Experiments using challenging images have been performed showing that the combination of both techniques is feasible and provides accurate estimates of the motion parameters even in the presence of strong illumination changes between the images.
This work has been partially funded with projects ESP2005-07724-C05 and CSD2007-00018 from the Spanish Ministry of Science and Education.
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Montoliu, R., Pla, F. (2008). Generalized Least Squares-Based Parametric Motion Estimation Under Non-uniform Illumination Changes. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_65
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DOI: https://doi.org/10.1007/978-3-540-69812-8_65
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