Dense Descriptors for Optical Flow Estimation: A Comparative Study
AbstractEstimating the displacements of intensity patterns between sequential frames is a very well-studied problem, which is usually referred to as optical flow estimation. The first assumption among many of the methods in the field is the brightness constancy during movements of pixels between frames. This assumption is proven to be not true in general, and therefore, the use of photometric invariant constraints has been studied in the past. One other solution can be sought by use of structural descriptors rather than pixels for estimating the optical flow. Unlike sparse feature detection/description techniques and since the problem of optical flow estimation tries to find a dense flow field, a dense structural representation of individual pixels and their neighbors is computed and then used for matching and optical flow estimation. Here, a comparative study is carried out by extending the framework of SIFT-flow to include more dense descriptors, and comprehensive comparisons are given. Overall, the work can be considered as a baseline for stimulating more interest in the use of dense descriptors for optical flow estimation. View Full-Text
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Baghaie, A.; D’Souza, R.M.; Yu, Z. Dense Descriptors for Optical Flow Estimation: A Comparative Study. J. Imaging 2017, 3, 12.
Baghaie A, D’Souza RM, Yu Z. Dense Descriptors for Optical Flow Estimation: A Comparative Study. Journal of Imaging. 2017; 3(1):12.Chicago/Turabian Style
Baghaie, Ahmadreza; D’Souza, Roshan M.; Yu, Zeyun. 2017. "Dense Descriptors for Optical Flow Estimation: A Comparative Study." J. Imaging 3, no. 1: 12.
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