Motion Estimation and Segmentation of Natural Phenomena

Da Chen; Wenbin Li; Peter Hall

Motion Estimation and Segmentation of Natural Phenomena

Research output: Contribution to conference › Paper › peer-review

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Abstract

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.

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	http://bmvc2018.org/

Keywords

natutal phenomena, tracking, segmentation

Access to Document

Chen_etal_bmvc2018.PDFAccepted author manuscript, 5.05 MB

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1 Active

Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)
Cosker, D., Campbell, N., Fincham Haines, T., Hall, P., Kim, K. I., Lutteroth, C., O'Neill, E., Richardt, C. & Yang, Y.
Engineering and Physical Sciences Research Council
1/09/15 → 28/02/21
Project: Research council

Cite this

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title = "Motion Estimation and Segmentation of Natural Phenomena",

abstract = "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.",

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AU - Li, Wenbin

AU - Hall, Peter

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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.

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M3 - Paper

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Y2 - 3 September 2018 through 6 September 2018

ER -

Motion Estimation and Segmentation of Natural Phenomena

Abstract

Conference

Keywords

Access to Document

Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)

Cite this