000 03926nam a22005655i 4500
001 978-3-319-30208-9
003 DE-He213
005 20200421111205.0
007 cr nn 008mamaa
008 160324s2016 gw | s |||| 0|eng d
020 _a9783319302089
_9978-3-319-30208-9
024 7 _a10.1007/978-3-319-30208-9
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aFish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data
_h[electronic resource] /
_cedited by Robert B. Fisher, Yun-Heh Chen-Burger, Daniela Giordano, Lynda Hardman, Fang-Pang Lin.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXVII, 319 p. 135 illus., 13 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v104
505 0 _aOverview 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.
520 _aThis 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aWildlife.
650 0 _aFish.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aFish & Wildlife Biology & Management.
700 1 _aFisher, Robert B.
_eeditor.
700 1 _aChen-Burger, Yun-Heh.
_eeditor.
700 1 _aGiordano, Daniela.
_eeditor.
700 1 _aHardman, Lynda.
_eeditor.
700 1 _aLin, Fang-Pang.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319302065
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v104
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-30208-9
912 _aZDB-2-ENG
942 _cEBK
999 _c54082
_d54082