@inproceedings{3b2d4886177849359a7761adf99596f3,
title = "Dense motion estimation for smoke",
abstract = "Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.",
author = "Da Chen and Wenbin Li and Peter Hall",
year = "2017",
month = mar,
day = "12",
doi = "10.1007/978-3-319-54190-7_14",
language = "English",
isbn = "9783319541891",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "225--239",
editor = "K. Nishino and S-H. Lai and V. Lepetit and Y. Sato",
booktitle = "Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers",
address = "USA United States",
note = "Asian Conference on Computer Vision 2016, ACCV2016 ; Conference date: 20-11-2016",
}