Abstract
| Original language | English |
|---|---|
| Publication status | Published - 4 Sep 2018 |
| Event | 29th British Machine Vision Conference 2018 - Northumbria University, Newcastle Duration: 3 Sep 2018 → 6 Sep 2018 http://bmvc2018.org/ |
Conference
| Conference | 29th British Machine Vision Conference 2018 |
|---|---|
| Abbreviated title | BMVC 2018 |
| City | Newcastle |
| Period | 3/09/18 → 6/09/18 |
| Internet address |
Keywords
- natutal phenomena, tracking, segmentation
Cite this
Motion Estimation and Segmentation of Natural Phenomena. / Chen, Da; Li, Wenbin; Hall, Peter.
2018. Paper presented at 29th British Machine Vision Conference 2018, Newcastle, .Research output: Contribution to conference › Paper
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TY - CONF
T1 - Motion Estimation and Segmentation of Natural Phenomena
AU - Chen, Da
AU - Li, Wenbin
AU - Hall, Peter
PY - 2018/9/4
Y1 - 2018/9/4
N2 - Dense motion estimation for dynamic natural phenomena (water, smoke, fire, etc.) is a significant open problem. Current approaches tend to be either general, giving poor results, or specialise in one phenomenon and fail to generalise. Segmentation of phenomena is also an open problem. This paper describes an approach to estimate dense motion for dynamic phenomena that is simple, general, and which yields state of the art results. We use our dense motion field to segment phenomena to above state of the art levels. We demonstrate our contributions using lab-based video, video from a public dataset, and from the internet.
AB - Dense motion estimation for dynamic natural phenomena (water, smoke, fire, etc.) is a significant open problem. Current approaches tend to be either general, giving poor results, or specialise in one phenomenon and fail to generalise. Segmentation of phenomena is also an open problem. This paper describes an approach to estimate dense motion for dynamic phenomena that is simple, general, and which yields state of the art results. We use our dense motion field to segment phenomena to above state of the art levels. We demonstrate our contributions using lab-based video, video from a public dataset, and from the internet.
KW - natutal phenomena, tracking, segmentation
M3 - Paper
ER -