Learning a convolutional neural network for propagation-based stereo image segmentation-Reference-Cited by-同舟云学术

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Learning a convolutional neural network for propagation-based stereo image segmentation

Published:2018-08-07 Issue:1 Volume:36 Page:39-52
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Li Xujie,Huang Hui,Zhao Hanli,Wang Yandan,Hu Mingxiao

Funder

Zhejiang Provincial Natural Science Foundation of China

National Natural Science Foundation of China

Open Project Program of the State Key Lab of CAD&CG

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s00371-018-1582-y.pdf

Reference39 articles.

1. Casaca, W., Nonato, L.G., Taubin, G.: Laplacian coordinates for seeded image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 384–391 (2014)

2. Peng, J., Shen, J., Jia, Y., Li, X.: Saliency cut in stereo images. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 22–28 (2013)

3. Tasli, H.E., Alatan, A.A.: User assisted disparity remapping for stereo images. Signal Process. Image Commun. 28(10), 1374–1389 (2013)

4. Ma, W., Qin, Y., Yang, L., Shibiao, X., Zhang, X.: Interactive stereo image segmentation with rgb-d hybrid constraints. IEEE Signal Process. Lett. 23(11), 1533–1537 (2016)

5. Ju, R., Ren, T., Wu, G.: Stereosnakes: contour based consistent object extraction for stereo images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1724–1732 (2015)

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