Abstract
Optical flow is defined as pixel motion between two images. Hence, in order to estimate optical flow, an energy model is proposed. This model considers: a data term and a regularization term. Data term is an optical flow error estimation and regularization term imposes spatial smoothness. Most of traditional variational models use a linearized version of data term, which fails when the displacement of the object is larger than their own size. Last years the precision of optical flow method has been increased due to the use of additional information, which comes from correspondences computed between two images obtained by: SIFT, Deep-matching or exhaustive search. This paper presents an experimental study to evaluate strategies for locating exhaustive correspondences improving flow estimation. We considered different location for matching: random location, uniform location, maximum of the gradient and maximum error of the optical flow estimation. Best performance (minimum EPE and AAE error) was obtained by the Uniform Location which outperforms reported results in the literature.
The original version of this chapter was revised. An erratum to this chapter can be found at https://doi.org/10.1007/978-3-319-77028-4_102
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Lazcano, V. (2018). Study of Specific Location of Exhaustive Matching in Order to Improve the Optical Flow Estimation. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-77028-4_77
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DOI: https://doi.org/10.1007/978-3-319-77028-4_77
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