Abstract
The deep-sea tripod systems are designed and built at the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center (PCMSC) in Santa Cruz, California. They are recovered in late September 2014 after spending about half a year collecting data on the floor of the South China Sea. The deep-sea tripod systems are named as Free-Ascending Tripod (FAT), are deployed at 2,100 m water depth—roughly 10 times as deep as most tripods dedicated to measuring currents and sediment movement at the seafloor. Deployment at this unusual depth was made possible by the tripod’s ability to rise by itself to the surface rather than being pulled up by a line. Instruments mounted on the tripod took bottom photographs and measured such variables as water temperature, current velocity, and suspended-sediment concentration. FAT is used to better understand how and where deep-seafloor sediment moves and accumulates. Besides of this, we also use them to study the deep-sea biology. The obtained the images from the camera, the biology animals are hardly to be distinguished. In this project, we are concerned to use novel underwater imaging technologies for recovering the deep-sea scene.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ocean Observations. http://en.wikipedia.org/wiki/ocean_observations
Ocean Networks Canada. http://www.oceannetworks.ca/
NOAA Ocean Climate Observation Program. http://www.oco.noaa.gov/
Japanese Ocean Flux Data Sets with Use of Remote Sensing Observation. http://dtsv.scc.u-tokai.ac.jp/j-ofuro/
Woods Hole Oceanographic Institution. http://www.whoi.edu/
Serikawa, S., Lu, H.: Underwater image dehazing using joint trilateral filter. Comput. Electr. Eng. 40(1), 41–50 (2014)
Lu, H., Li, Y., Zhang, Y., Chen, M., Serikawa, S., Kim, H.: Underwater optical image processing: a comprehensive review. Mob. Netw. Appl. 1–8 (2017)
Lu, H., Li, Y., Uemura, T., Ge, Z., Xu, X., He, L., Serikawa, S., Kim, H.: FDCNet: filtering deep convolutional network for marine organism classification. Multimed. Tools Appl. 1–14 (2017)
Lu, H., Li, Y., Nakashima, S., Kim, H., Serikawa, S.: Underwater image super-resolution by descattering and fusion. IEEE Access 5, 670–679 (2017)
Li, Y., Lu, H., Li, J., Li, X., Li, Y., Serikawa, S.: Underwater image de-scattering and classification by deep neural network. Comput. Electr. Eng. 54, 68–77 (2016)
Lu, H., Li, B., Zhu, J., Li, Y., Li, Y., Xu, X., He, L., Li, X., Li, J., Serikawa, S.: Wound intensity correction and segmentation with convolutional neural networks. Concurr. Comput.: Pract. Exp. 27(9), 1–10 (2017)
Lu, H., Li, Y., Nakashima, S., Serikawa, S.: Turbidity underwater image restoration using spectral properties and light compensation. IEICE Trans. Inf. Syst. 99(1), 219–227 (2016)
Lu, H., Li, Y., Zhang, L., Serikawa, S.: Contrast enhancement for images in turbid water. JOSA 32(5), 886–893 (2015)
Li, Y., Lu, H., Zhang, L., Li, J., Serikawa, S.: Real-time visualization system for deep-sea surveying. Math. Probl. Eng. 1–10 (2014)
Lu, H., Zhang, L., Serikawa, S.: Maximum local energy: an effective approach for multisensor image fusion in beyond wavelet transform domain. Comput. Math Appl. 64(5), 996–1003 (2012)
Acknowledgements
This work was supported by Leading Initiative for Excellent Young Researcher of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Grants-in-Aid for Scientific Research of JSPS (17K14694), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1803), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1510; 1315), Research Fund of The Telecommunications Advancement Foundation, Fundamental Research Developing Association for Shipbuilding and Offshore, Japan-China Scientific Cooperation Program (6171101454), International Exchange Program of National Institute of Information and Communications (NICT), and Collaboration Program of National Institute of Informatics (NII).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lu, H., Li, Y., Kim, H., Serikawa, S. (2020). DeepEye: A Dedicated Camera for Deep-Sea Tripod Observation Systems. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-04946-1_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04945-4
Online ISBN: 978-3-030-04946-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)