FDCNet: filtering deep convolutional network for marine organism classification
FDCNet: filtering deep convolutional network for marine organism classification
About this item
Full title
Author / Creator
Lu, Huimin , Li, Yujie , Uemura, Tomoki , Ge, Zongyuan , Xu, Xing , He, Li , Serikawa, Seiichi and Kim, Hyoungseop
Publisher
New York: Springer US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Springer US
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Scope and Contents
Contents
Convolutional networks are currently the most popular computer vision methods for a wide variety of applications in multimedia research fields. Most recent methods have focused on solving problems with natural images and usually use a training database, such as Imagenet or Openimage, to detect the characteristics of the objects. However, in practic...
Alternative Titles
Full title
FDCNet: filtering deep convolutional network for marine organism classification
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2086976768
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2086976768
Other Identifiers
ISSN
1380-7501
E-ISSN
1573-7721
DOI
10.1007/s11042-017-4585-1
