YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Journal of Atmospheric and Oceanic Technology
    • View Item
    •   YE&T Library
    • AMS
    • Journal of Atmospheric and Oceanic Technology
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital In-Line Holography

    Source: Journal of Atmospheric and Oceanic Technology:;2015:;volume( 032 ):;issue: 006::page 1241
    Author:
    Davies, Emlyn J.
    ,
    Buscombe, Daniel
    ,
    Graham, George W.
    ,
    Nimmo-Smith, W. Alex M.
    DOI: 10.1175/JTECH-D-14-00157.1
    Publisher: American Meteorological Society
    Abstract: ubstantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing, and classification of particles. These computational challenges are the subject of this paper, in which the authors draw upon data collected using a variety of holographic systems developed at Plymouth University, United Kingdom, from a significant range of particle types, sizes, and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from oversegmentation of particles. A simple unsupervised particle classification system is developed and is capable of successfully differentiating sand grains, bubbles, and diatoms from within the surfzone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments, and oil droplets.
    • Download: (2.358Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Statistics

      Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital In-Line Holography

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4228579
    Collections
    • Journal of Atmospheric and Oceanic Technology

    Show full item record

    contributor authorDavies, Emlyn J.
    contributor authorBuscombe, Daniel
    contributor authorGraham, George W.
    contributor authorNimmo-Smith, W. Alex M.
    date accessioned2017-06-09T17:26:00Z
    date available2017-06-09T17:26:00Z
    date copyright2015/06/01
    date issued2015
    identifier issn0739-0572
    identifier otherams-85162.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4228579
    description abstractubstantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing, and classification of particles. These computational challenges are the subject of this paper, in which the authors draw upon data collected using a variety of holographic systems developed at Plymouth University, United Kingdom, from a significant range of particle types, sizes, and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from oversegmentation of particles. A simple unsupervised particle classification system is developed and is capable of successfully differentiating sand grains, bubbles, and diatoms from within the surfzone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments, and oil droplets.
    publisherAmerican Meteorological Society
    titleEvaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital In-Line Holography
    typeJournal Paper
    journal volume32
    journal issue6
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH-D-14-00157.1
    journal fristpage1241
    journal lastpage1256
    treeJournal of Atmospheric and Oceanic Technology:;2015:;volume( 032 ):;issue: 006
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian