Abstract
We present a method based on Kalman filtering, for image motion estimation. Within Kalman formalism, a motion boundary can be modelled as a jump in the evolution equation of the filter. The detection of such a jump relies on a χ2 statistical test applied to the innovation signal. The optimal estimation of the jump parameters and the compensation of the current estimate are performed using a General Likelihood Ratio (GLR) algorithm. To exploit the spatial redundancy inherent to a motion boundary, the original GLR algorithm is reformulated by integrating spatiotemporal motion information. This results in a significant decrease of the compensation delay.
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© 1994 Springer-Verlag Berlin Heidelberg
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Germain, F., Skordas, T. (1994). An image motion estimation technique based on a combined statistical test and spatiotemporal generalised likelihood ratio approach. In: Eklundh, JO. (eds) Computer Vision — ECCV '94. ECCV 1994. Lecture Notes in Computer Science, vol 800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57956-7_17
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DOI: https://doi.org/10.1007/3-540-57956-7_17
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