Transferring deep knowledge for object recognition in Low-quality underwater videos
By a News Reporter-Staff News Editor at Life Science Weekly -- Investigators publish new report on Carrier Proteins - Iron-Binding Proteins. According to news reporting originating from Changchun, People’s Republic of China, by NewsRx correspondents, research stated, “In recent years, underwater video technologies allow us to explore the ocean in scientific and noninvasive ways, such as environmental monitoring, marine ecology studies, and fisheries management. However the low-light and high-noise scenarios pose great challenges for the underwater image and video analysis.”
Financial supporters for this research include National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Shandong Province.
Our news editors obtained a quote from the research from the Chinese Academy of Sciences, “We here propose a CNN knowledge transfer framework for underwater object recognition and tackle the problem of extracting discriminative features from relatively low contrast images. Even with the insufficient training set, the transfer framework can well learn a recognition model for the special underwater object recognition task together with the help of data augmentation. For better identifying objects from an underwater video, a weighted probabilities decision mechanism is introduced to identify the object from a series of frames. The proposed framework can be implemented for real-time underwater object recognition on autonomous underwater vehicles and video monitoring systems. To verify the effectiveness of our method, experiments on a public dataset are carried out.”
According to the news editors, the research concluded: “The results show that the proposed method achieves promising results for underwater object recognition on both test image datasets and underwater videos.”
For more information on this research see: Transferring deep knowledge for object recognition in Low-quality underwater videos. Neurocomputing , 2018;275():897-908. Neurocomputing can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Neurocomputing - http://www.journals.elsevier.com/neurocomputing/)
The news editors report that additional information may be obtained by contacting X. Sun, Chinese Academy Sci, State Key Lab Appl Opt, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, People’s Republic of China. Additional authors for this research include J.Y. Shi, L.P. Liu, J.Y. Dong, C. Plant, X.H. Wang and H.Y. Zhou.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.neucom.2017.09.044. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-01-23), Chinese Academy of Sciences Reports Findings in Iron-Binding Proteins (Transferring deep knowledge for object recognition in Low-quality underwater videos), Life Science Weekly, 806, ISSN: 1552-2474, BUTTER® ID: 015026729
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