國立虎尾科技大學 |

Automated Analysis Techniques for Oceanographic Imaging.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Automated Analysis Techniques for Oceanographic Imaging./
Author:	Orenstein, Eric Coughlin.
Description:	1 online resource (125 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
Subject:	Ocean engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355650563

Automated Analysis Techniques for Oceanographic Imaging.
Orenstein, Eric Coughlin.

Automated Analysis Techniques for Oceanographic Imaging. - 1 online resource (125 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2018.

Includes bibliographical references

Since scientists began to study the ocean, they have had to develop new observational methods. Modern marine biologists and ecologists have increasingly used imaging systems to help address their most persistent and vexing questions. These techniques have allowed researchers to take samples at higher spatial or temporal frequency than ever before. While yielding marvelous new insight into difficult to observe phenomena, image-based sampling creates a new data problem: there is too much of it. Without techniques to classify and analyze oceanographic images, much of the data sits idle.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355650563Subjects--Topical Terms:

857658
Ocean engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Automated Analysis Techniques for Oceanographic Imaging.
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100 1 $a Orenstein, Eric Coughlin. $3 1190423
245 1 0 $a Automated Analysis Techniques for Oceanographic Imaging.
264 0 $c 2018
300 $a 1 online resource (125 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Adviser: Jules S. Jaffe.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2018.
504 $a Includes bibliographical references
520 $a Since scientists began to study the ocean, they have had to develop new observational methods. Modern marine biologists and ecologists have increasingly used imaging systems to help address their most persistent and vexing questions. These techniques have allowed researchers to take samples at higher spatial or temporal frequency than ever before. While yielding marvelous new insight into difficult to observe phenomena, image-based sampling creates a new data problem: there is too much of it. Without techniques to classify and analyze oceanographic images, much of the data sits idle.
520 $a Two instruments developed in the Jaffe Laboratory for Underwater Imaging are em- blematic of this trend. The Sub Sea Holodeck (SSH) is an immersive virtual aquarium developed to study cephalopod camouflage in the laboratory. The Scripps Plankton Camera System (SPCS) is a pair of in situ microscopes built to observe undisturbed plankton populations over long periods of time. In this thesis, new techniques, grounded in current machine learning methodologies, are developed to speed the analysis of these information rich big data sets.
520 $a Data from the SSH are treated as textures and classified using texton dictionaries. In addition to being highly accurate, the texton-based method is entirely data driven---the metric for separating it is derived directly from the images. As a consequence, a new criterion for classifying cephalopods is proposed that explicitly treats samples that do not conform to the prescribed three class system.
520 $a Images from the SPCS and several other in situ plankton imaging systems are used to evaluate the performance of new deep learning methods. Several Convolutional Neural Networks (CNNs) are trained from the data and tested against each other. The results underscore the startling representational power of CNNs and suggest that neural methods outperform other approaches for annotating plankton images.
520 $a Finally, an automated classifier is deployed on SPCS data to explore the dynamics of a host-parasite relationship. A CNN is used to build a high-resolution time series tracking fluctuations in Oithona similis and the parasite Paradinium sp. The subsequent analysis identifies a possible time scale of the parasite's internal life stage and its effect on the overall O. similis population.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Ocean engineering. $3 857658
650 4 $a Computer science. $3 573171
650 4 $a Biological oceanography. $3 1178855
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0984
690 $a 0416
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Oceanography. $3 1185907
773 0 $t Dissertation Abstracts International $g 79-07B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10744643 $z click for full text (PQDT)