Jiaxing Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jiaxing Wang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Deqiang Wang
School of Information Science and Engineering, Shandong University, Qingdao, China
1 shared publication - Jinchang Ren
Guangdong Polytechnic Normal University
1 shared publication - Kidiyo Kpalma
Univ Rennes, INSA Rennes, CNRS, IETR-UMR 6164, F-35000 Rennes, France
1 shared publication - Mingqiang Yang
Shandong University
1 shared publication - Qinghe Zheng
School of Intelligent Engineering, Shandong Management University, Jinan 250357, China
1 shared publication - Zhongjun Ding
National Deep Sea Center, Qingdao 266237, China
1 shared publication
Latest Publications
- Open Access
Detection of the Deep-Sea Plankton Community in Marine Ecosystem with Underwater Robotic Platform
Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel method is introduced for deep-sea plankton community detection in marine ecosystem using an underwater robotic platform. The videos were sampled at a distance of 1.5 m from the ocean floor, with a focal length of 1.5–2.5 m. The optical flow field is used to detect plankton community. We showed that for each of the moving plankton that do not overlap in space in two consecutive video frames, the time gradient of the spatial position of the plankton are opposite to each other in two consecutive optical flow fields. Further, the lateral and vertical gradients have the same value and orientation in two consecutive optical flow fields. Accordingly, moving plankton can be accurately detected under the complex dynamic background in the deep-sea environment. Experimental comparison with manual ground-truth fully validated the efficacy of the proposed methodology, which outperforms six state-of-the-art approaches.
Open AccessDetection of the Deep-Sea Plankton Community in Marine Ecosystem with Underwater Robotic Platform
Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel method is introduced for deep-sea plankton community detection in marine ecosystem using an underwater robotic platform. The videos were sampled at a distance of 1.5 m from the ocean floor, with a focal length of 1.5–2.5 m. The optical flow field is used to detect plankton community. We showed that for each of the moving plankton that do not overlap in space in two consecutive video frames, the time gradient of the spatial position of the plankton are opposite to each other in two consecutive optical flow fields. Further, the lateral and vertical gradients have the same value and orientation in two consecutive optical flow fields. Accordingly, moving plankton can be accurately detected under the complex dynamic background in the deep-sea environment. Experimental comparison with manual ground-truth fully validated the efficacy of the proposed methodology, which outperforms six state-of-the-art approaches.