Abstract:
With the increasing incorporation of cameras for electronic monitoring (EM) in the existing underwater/on-the boat fishery-dependent data collection programs, more and more image/video data are being collected for underwater fish stock estimation, catch accounting and/or compliance with catch retention requirements. Moreover, they can also be used to enable a non-extractive and non-lethal approach to fisheries survey and abundance estimation. The camera-based monitoring and sampling approaches not only can conserve depleted fish stocks but also provides an effective way to analyze a greater diversity of marine animals, resulting in a better understanding of the health status of ocean. This approach, however, generates vast amounts of image/video data very rapidly, effective image/video and machine learning techniques to handle these big visual data are thus critically required to make such monitoring and sampling approaches practical.In this talk I will first review some popular and effective image/vision analysis techniques and machine learning algorithms (e.g., supervised learning, deep learning, semi-supervised and query learning), which can provide cost-effective solutions for collecting fishery dependent data to meet the needs of a range of scientific, management, and compliance objectives. I will then report some progresses jointly made with AFSC of NOAA to develop a live fish counting, catch event detection, length measurement and species recognition system, based on the data collected, using the underwater Camtrawl or UAV, as well as on-board chute or rail-fishing camera systems.
Biography of Jenq-Neng Hwang:
Dr. Jenq-Neng Hwang received the BS and MS degrees, both in electrical engineering from the National Taiwan University, Taipei, Taiwan, in 1981 and 1983 separately. He then received his Ph.D. degree from the University of Southern California. In the summer of 1989, Dr. Hwang joined the Department of Electrical Engineering of the University of Washington in Seattle, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research from 2003 to 2005, and from2011-2015. He is currently the Associate Chair for Global Affairs and International Development in the EE Department. He has written more than 300 journal, conference papers and book chapters in the areas of multimedia signal processing, and multimedia system integration and networking, including an authored textbook on "Multimedia Networking: from Theory to Practice," published by Cambridge University Press. Dr. Hwang has close working relationship with the industry on multimedia signal processing and multimedia networking.Dr. Hwang received the 1995 IEEE Signal Processing Society's Best Journal Paper Award. He is a founding member of Multimedia Signal Processing Technical Committee of IEEE Signal Processing Society and was the Society's representative to IEEE Neural Network Council from 1996 to 2000. He is currently a member of Multimedia Technical Committee (MMTC) of IEEE Communication Society and also a member of Multimedia Signal Processing Technical Committee (MMSP TC) of IEEE Signal Processing Society. He served as associate editors for IEEE T-SP, T-NN and T- CSVT, T-IP and Signal Processing Magazine (SPM). He is currently on the editorial board of ZTE Communications, ETRI, IJDMB and JSPS journals. He served as the Program Co-Chair of IEEE ICME 2016 and was the Program Co-Chairs of ICASSP1998 and ISCAS 2009. Dr. Hwang is a fellow of IEEE since 2001.
