Abstract
Motion estimation is a fundamental problem in many computer vision applications. One solution to this problem consists in defining a large enough set of candidate motion vectors, and using a combinatorial optimization algorithm to find, for each point of interest, the candidate which best represents the motion at the point of interest. The choice of the candidate set has a direct impact in the accuracy and computational complexity of the optimization method. In this work, we show that a set containing the most representative maxima of the phase-correlation function between the two input images, computed for different overlapping regions, provides better accuracy and contains less spurious candidates than other choices in the literature. Moreover, a pre-selection stage, based in a local motion estimation algorithm, can be used to further reduce the cardinality of the candidate set, without affecting the accuracy of the results.
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Reyes, A., Alba, A., Arce-Santana, E. (2014). Efficiency Analysis of POC-Derived Bases for Combinatorial Motion Estimation. In: Klette, R., Rivera, M., Satoh, S. (eds) Image and Video Technology. PSIVT 2013. Lecture Notes in Computer Science, vol 8333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53842-1_11
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DOI: https://doi.org/10.1007/978-3-642-53842-1_11
Publisher Name: Springer, Berlin, Heidelberg
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