CoralSeg: Learning coral segmentation from sparse annotations-Reference-Cited by-同舟云学术

CoralSeg: Learning coral segmentation from sparse annotations

Published:2019-10-10 Issue:8 Volume:36 Page:1456-1477
ISSN:1556-4959
Container-title:Journal of Field Robotics
language:en
Short-container-title:J Field Robotics

Author:

Alonso Iñigo¹^ORCID,Yuval Matan²³,Eyal Gal⁴⁵,Treibitz Tali²,Murillo Ana C.¹

Affiliation:

1. Aragón Institute for Engineering Research (I3A)University of ZaragozaZaragoza Spain

2. Leon H. Charney School of Marine SciencesUniversity of HaifaHaifa Israel

3. The Interuniversity Institute for Marine Sciences of EilatEilat Israel

4. ARC Centre of Excellence for Coral Reef Studies, School of Biological SciencesThe University of QueenslandQueensland Australia

5. The Mina & Everard Goodman Faculty of Life SciencesBar‐Ilan UniversityRamatr‐Gan Israel

Publisher

Wiley

Subject

Computer Science Applications,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21915

Reference91 articles.

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3. Akkaynak D. &Treibitz T.(2019). Sea‐thru: A method for removing water from underwater images. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.1682–1691.

4. Alonso I. Cambra A. Munoz A. Treibitz T. &Murillo A. C.(2017). Coral‐segmentation: Training dense labeling models with sparse ground truth. InIEEE International Conference on Computer Vision Workshops pp. 2874–2882.

5. Alonso I. &Murillo A. C.(2018).Semantic Segmentation from Sparse Labeling Using Multi‐Level Superpixels. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(pp. 5785‐5792).

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