Abstract
We describe a new method of achieving autocalibration that uses a stochastic optimization approach taken from the field of evolutionary computing and we perform a number of experiments on standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, i.e. they are taken from the same camera without varying the focal length. We show that for the autocalibration of focal length and aspect ratio, the evolutionary method achieves comparable results without the implementation complexity of other methods. Autocalibrating from the fundamental matrix is simply transformed into a global minimization problem utilizing a cost function based on the properties of the fundamental matrix and the essential matrix.
Partially funded by Nortel Networks Scholarship.
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Whitehead, A., Roth, G. (2002). Evolutionary Based Autocalibration from the Fundamental Matrix. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_29
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DOI: https://doi.org/10.1007/3-540-46004-7_29
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