AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Computational Visual Media Article
PDF (1.2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
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.
Show Author Information

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

References

[1]
J. Long,; E. Shelhamer,; T. Darrell, Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440, 2015.
Crossref
[2]
S. Caelles,; K. K. Maninis,; J. Pont-Tuset,; L. Leal-Taixé,; D. Cremers,; L. Van Gool, One-shot video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 221-230, 2017.
Crossref
[3]
F. Perazzi,; A. Khoreva,; R. Benenson,; B. Schiele,; A. Sorkine-Hornung, Learning video object segmentation from static images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2663-2672, 2017.
Crossref
[4]
A. Khoreva,; R. Benenson,; E. Ilg,; T. Brox,; B. Schiele, Lucid data dreaming for multiple object tracking. arXiv preprint arXiv:1703.09554, 2017.
[5]
P. Voigtlaender,; B. Leibe, Online adaptation of convolutional neural networks for video object segmentation. In: Proceedings of the British Machine Vision Conference, 116.1-116.13, 2017.
Crossref
[6]
Y.-T. Hu,; J.-B. Huang,; A. G. Schwing, MaskRNN: Instance level video object segmentation. In: Proceedings of the 31st Conference on Neural Information Processing Systems, 325-334, 2017.
[7]
J. Luiten,; P. Voigtlaender,; B. Leibe, PReMVOS: Proposal-generation, refinement and merging for video object segmentation. In: Computer Vision - ACCV 2018. Lecture Notes in Computer Science, Vol. 11364. C. Jawahar,; H. Li,; G. Mori,; K. Schindler, Eds. Springer Cham, 565-580, 2018.
Crossref
[8]
J. C. Cheng,; Y. H. Tsai,; W. C. Hung,; S. J. Wang,; M. H. Yang, Fast and accurate online video object segmentation via tracking parts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7415-7424, 2018.
Crossref
[9]
K. K. Maninis,; S. Caelles,; Y. Chen,; J. Pont-Tuset,; L. Leal-Taixe,; D. Cremers,; L. Van Gool, Video object segmentation without temporal information. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 41, No. 6, 1515-1530, 2019.
Crossref Google Scholar
[10]
L. J. Yang,; Y. R. Wang,; X. H. Xiong,; J. C. Yang,; A. K. Katsaggelos, Efficient video object segmentation via network modulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6499-6507, 2018.
Crossref
[11]
X. X. Li,; C. C. Loy, Video object segmentation with joint re-identification and attention-aware mask propagation. In: Computer Vision - ECCV 2018. Lecture Notes in Computer Science, Vol. 11207. V. Ferrari,; M. Hebert,; C. Sminchisescu,; Y. Weiss, Eds. Springer Cham, 93-110, 2018.
[12]
J. S. Yoon,; F. Rameau,; J. Kim,; S. Lee,; S. Shin,; I. S. Kweon, Pixel-level matching for video object segmentation using convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, 2186-2195, 2017.
Crossref
[13]
Y. H. Chen,; J. Pont-Tuset,; A. Montes,; L. Van Gool, Blazingly fast video object segmentation with pixel-wise metric learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1189-1198, 2018.
Crossref
[14]
Y. T. Hu,; J. B. Huang,; A. G. Schwing, VideoMatch: Matching based video object segmentation. In: Computer Vision - ECCV 2018. Lecture Notes in Computer Science, Vol. 11212. V. Ferrari,; M. Hebert,; C. Sminchisescu,; Y. Weiss, Eds. Springer Cham, 56-73, 2018.
[15]
P. Voigtlaender,; Y. N. Chai,; F. Schroff,; H. Adam,; B. Leibe,; L. C. Chen, FEELVOS: Fast end-to-end embedding learning for video object segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9473-9482, 2019.
Crossref
[16]
S. W. Oh,; J. Y. Lee,; K. Sunkavalli,; S. J. Kim, Fast video object segmentation by reference-guided mask propagation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7376-7385, 2018.
Crossref
[17]
K. M. He,; X. Y. Zhang,; S. Q. Ren,; J. Sun, Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778, 2016.
Crossref
[18]
B. L. Zhou,; H. Zhao,; X. Puig,; S. Fidler,; A. Barriuso,; A. Torralba, Scene parsing through ADE20K dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5122-5130, 2017.
Crossref
[19]
L. Lopez-Fuentes,; C. Rossi,; H. Skinnemoen, River segmentation for flood monitoring. In: Proceedings of the IEEE International Conference on Big Data, 3746-3749, 2017.
Crossref
[20]
Farson digital watercams. https://www.farsondigitalwatercams.com/. Accessed: 2019-09-30.
[21]
J. Pont-Tuset,; F. Perazzi,; S. Caelles,; P. Arbeláez,; A. Sorkine-Hornung,; L. Van Gool, The 2017 davis challenge on video object segmentation. arXiv preprint arXiv:1704.00675, 2017.
[22]
S. Caelles,; J. Pont-Tuset,; F. Perazzi,; A. Montes,; K. K. Maninis,; L. Van Gool, The 2019 davis challenge on vos: Unsupervised multi-object segmentation. arXiv preprint arXiv:1905.00737, 2019.
[23]
F. Perazzi,; J. Pont-Tuset,; B. McWilliams,; L. Van Gool,; M. Gross,; A. Sorkine-Hornung, A benchmark dataset and evaluation methodology for video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 724-732, 2016.
Crossref
Computational Visual Media
Volume 6 Issue 1,
March 2020
Pages 65-78
DOI: 10.1007/s41095-020-0156-x
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

582

Views

85

Downloads

8

Crossref

N/A

Web of Science

16

Scopus

0

CSCD

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

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www. editorialmanager.com/cvmj.
Return