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Research Article | Open Access

WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance

School of Electrical Engineering and Computer Science, Louisiana State University, USA.
Department of Civil Engineering, Louisiana State University, USA.
Department of Mechanical Engineering, Zhejiang University, China.
Department of Civil Engineering, Northeastern University, USA.
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Abstract

We develop a novel network to segment water with significant appearance variation in videos. Unlike existing state-of-the-art video segmentation approaches that use a pre-trained feature recognition network and several previous frames to guide seg-mentation, we accommodate the object’s appearance variation by considering features observed from the current frame. When dealing with segmentation of objects such as water, whose appearance is non-uniform and changing dynamically, our pipeline can produce more reliable and accurate segmentation results than existing algorithms.

Keywords

video segmentationwater segmentationappearance adaptation

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Computational Visual Media
Volume 6 Issue 1,
March 2020
Pages 65-78
DOI: 10.1007/s41095-020-0156-x

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Cite this article:
Liang Y, Jafari N, Luo X, et al. WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance. Computational Visual Media, 2020, 6(1): 65-78. https://doi.org/10.1007/s41095-020-0156-x

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Revised: 16 January 2020
Accepted: 25 January 2020
Published: 23 March 2020
© The Author(s) 2020

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