Abstract
The approaches to global motion estimation have been naturally classified into one of two main classes: feature-based methods and direct (or featureless) methods. Feature-based methods compute a set of point correspondences between the images and, from these, estimate the parameters describing the global motion. Although the simplicity of the second step has made this approach rather appealing, the correspondence step is a quagmire and usually requires human supervision. In opposition, featureless methods attempt to estimate the global motion parameters directly from the image intensities, using complex nonlinear optimization algorithms. In this paper, we propose an iterative scheme that combines the feature-based simplicity with the featureless robustness. Our experiments illustrate the behavior of the proposed scheme and demonstrate its effectiveness by automatically building image mosaics.
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Guerreiro, R.F.C., Aguiar, P.M.Q. (2006). Global Motion Estimation: Feature-Based, Featureless, or Both ?!. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_66
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DOI: https://doi.org/10.1007/11867586_66
Publisher Name: Springer, Berlin, Heidelberg
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