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J. Imaging 2017, 3(1), 12; doi:10.3390/jimaging3010012

Dense Descriptors for Optical Flow Estimation: A Comparative Study

1
Department of Electrical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
2
Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
3
Departments of Computer Science and Electrical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Philip Morrow
Received: 17 October 2016 / Revised: 12 January 2017 / Accepted: 22 February 2017 / Published: 25 February 2017
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Abstract

Estimating 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
Keywords: feature descriptors; dense descriptors; optical flow estimation feature descriptors; dense descriptors; optical flow estimation
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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.

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