Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Fisher, Robert B. (Επιμελητής έκδοσης), Chen-Burger, Yun-Heh (Επιμελητής έκδοσης), Giordano, Daniela (Επιμελητής έκδοσης), Hardman, Lynda (Επιμελητής έκδοσης), Lin, Fang-Pang (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Έκδοση: 1st ed. 2016.
• Σειρά: Intelligent Systems Reference Library, 104
• Θέματα: Engineering.; Artificial intelligence.; Wildlife.; Fish.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).; Fish & Wildlife Biology & Management.

 This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and tracking, fish species recognition and analysis, a large SQL database to record the results and an efficient retrieval mechanism. Novel user interface mechanisms were developed to provide easy access for marine ecologists, who wanted to explore the dataset. The book is a useful resource for system builders, as it gives an overview of the many new methods that were created to build the Fish4Knowledge system in a manner that also allows readers to see how all the components fit together. 

 Overview of the Fish4Knowledge Project -- User Information Needs -- Supercomputing Resources -- Marine Video Data Capture and Storage -- Logical Data Resource Storage -- Software Architecture with Flexibility for the Data-Intensive Fish4Knowledge Project -- Fish4Knowledge Database Structure, Creating and Sharing Scientific Data) -- Intelligent Workflow Management for Fish4Knowledge using the SWELL System -- Fish Detection -- Fish Tracking -- Hierarchical Classification System with Reject Option for Live Fish Recognition -- Fish Behavior Analysis -- Understanding Uncertainty Issues in the Exploration of Fish Counts -- Data Groundtruthing and Crowdsourcing -- Counting on Uncertainty: Obtaining Fish Counts from Machine Learning Decisions -- Experiments with the Full Fish4Knowledge Dataset -- The Fish4Knowledge Virtual World Gallery -- Conclusions.