19.6.3.7.5 Plankton Analysis, Extraction, Features, Small Scale and Large Scale

Chapter Contents (Back)
Plankton. Application, Plankton. See also Suspended Particulate Matter, Turbidity, Water Areas.

Tang, X.[Xiaoou], Stewart, W.K.[W. Kenneth], Huang, H.[He], Gallager, S.M.[Scott M.], Davis, C.S.[Cabell S.], Vincent, L.[Luc], Marra, M.[Marty],
Automatic Plankton Image Recognition,
AIR(12), No. 1-3, February 1998, pp. 177-199.
WWW Link. 9807
Applied to detection and monitoring of plankton. BibRef

d'Alimonte, D., Zibordi, G.,
Phytoplankton determination in an optically complex coastal region using a multilayer perceptron neural network,
GeoRS(41), No. 12, December 2003, pp. 2861-2868.
IEEE Abstract. 0402
BibRef

Luo, T., Kramer, K., Goldgof, D.B., Hall, L.O., Samson, S., Remsen, A., Hopkins, T.,
Recognizing Plankton Images From the Shadow Image Particle Profiling Evaluation Recorder,
SMC-B(34), No. 4, August 2004, pp. 1753-1762.
IEEE Abstract. 0409
BibRef
And: Errata: SMC-B(34), No. 6, December 2004, pp. 2423-2423.
IEEE Abstract. 0412
BibRef

Verikas, A.[Antanas], Gelzinis, A.[Adas], Bacauskiene, M.[Marija], Olenina, I., Olenin, S., Vaiciukynas, E.[Evaldas],
Phase congruency-based detection of circular objects applied to analysis of phytoplankton images,
PR(45), No. 4, 2012, pp. 1659-1670.
Elsevier DOI 1410
Phase congruency BibRef

Gelzinis, A.[Adas], Verikas, A.[Antanas], Vaiciukynas, E.[Evaldas], Bacauskiene, M.[Marija],
A novel technique to extract accurate cell contours applied for segmentation of phytoplankton images,
MVA(26), No. 2-3, April 2015, pp. 305-315.
Springer DOI 1504
BibRef

Rousseaux, C.S.[Cecile S.], Gregg, W.W.[Watson W.],
Interannual Variation in Phytoplankton Primary Production at A Global Scale,
RS(6), No. 1, 2013, pp. 1-19.
DOI Link 1412
BibRef

Ryan, J.P.[John P.], Davis, C.O.[Curtiss O.], Tufillaro, N.B.[Nicholas B.], Kudela, R.M.[Raphael M.], Gao, B.C.[Bo-Cai],
Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,
RS(6), No. 2, 2014, pp. 1007-1025.
DOI Link 1403
See also Correction: Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA. BibRef

Montes, M.J.[Marcos J.], Ryan, J.P.[John P.], Davis, C.O.[Curtiss O.], Tufillaro, N.B.[Nicholas B.], Kudela, R.M.[Raphael M.],
Correction: Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,
RS(7), No. 10, 2015, pp. 13364.
DOI Link 1511
See also Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA. BibRef

Siswanto, E.[Eko], Tanaka, K.[Katsuhisa],
Phytoplankton Biomass Dynamics in the Strait of Malacca within the Period of the SeaWiFS Full Mission: Seasonal Cycles, Interannual Variations and Decadal-Scale Trends,
RS(6), No. 4, 2014, pp. 2718-2742.
DOI Link 1405
BibRef

Blondeau-Patissier, D.[David], Schroeder, T.[Thomas], Brando, V.E.[Vittorio E.], Maier, S.W.[Stefan W.], Dekker, A.G.[Arnold G.], Phinn, S.[Stuart],
ESA-MERIS 10-Year Mission Reveals Contrasting Phytoplankton Bloom Dynamics in Two Tropical Regions of Northern Australia,
RS(6), No. 4, 2014, pp. 2963-2988.
DOI Link 1405
BibRef

Brewin, R.J.W.[Robert J.W.], Mélin, F.[Frédéric], Sathyendranath, S.[Shubha], Steinmetz, F.[François], Chuprin, A.[Andrei], Grant, M.[Mike],
On the temporal consistency of chlorophyll products derived from three ocean-colour sensors,
PandRS(97), No. 1, 2014, pp. 171-184.
Elsevier DOI 1410
Phytoplankton BibRef

Soppa, M.A.[Mariana A.], Hirata, T.[Takafumi], Silva, B.[Brenner], Dinter, T.[Tilman], Peeken, I.[Ilka], Wiegmann, S.[Sonja], Bracher, A.[Astrid],
Global Retrieval of Diatom Abundance Based on Phytoplankton Pigments and Satellite Data,
RS(6), No. 10, 2014, pp. 10089-10106.
DOI Link 1411
BibRef

Xi, H.Y.[Hong-Yan], Hieronymi, M.[Martin], Röttgers, R.[Rüdiger], Krasemann, H.[Hajo], Qiu, Z.[Zhongfeng],
Hyperspectral Differentiation of Phytoplankton Taxonomic Groups: A Comparison between Using Remote Sensing Reflectance and Absorption Spectra,
RS(7), No. 11, 2015, pp. 14781.
DOI Link 1512
BibRef

Xue, K.[Kun], Zhang, Y.[Yuchao], Duan, H.T.[Hong-Tao], Ma, R.[Ronghua], Loiselle, S.[Steven], Zhang, M.[Minwei],
A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake,
RS(7), No. 11, 2015, pp. 14403.
DOI Link 1512
BibRef

Cristina, S.[Sónia], Cordeiro, C.[Clara], Lavender, S.[Samantha], Goela, P.C.[Priscila Costa], Icely, J.[John], Newton, A.[Alice],
MERIS Phytoplankton Time Series Products from the SW Iberian Peninsula (Sagres) Using Seasonal-Trend Decomposition Based on Loess,
RS(8), No. 6, 2016, pp. 449.
DOI Link 1608
BibRef

Wolanin, A.[Aleksandra], Soppa, M.A.[Mariana A.], Bracher, A.[Astrid],
Investigation of Spectral Band Requirements for Improving Retrievals of Phytoplankton Functional Types,
RS(8), No. 10, 2016, pp. 871.
DOI Link 1609
BibRef

Liu, Z.H.[Zong-Hua], Watson, J.[John], Allen, A.[Alastair],
A polygonal approximation of shape boundaries of marine plankton based-on genetic algorithms,
JVCIR(41), No. 1, 2016, pp. 305-313.
Elsevier DOI 1612
Image processing BibRef

Deng, Y.B.[Yu-Bing], Zhang, Y.L.[Yun-Lin], Li, D.[Deping], Shi, K.[Kun], Zhang, Y.[Yibo],
Temporal and Spatial Dynamics of Phytoplankton Primary Production in Lake Taihu Derived from MODIS Data,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef


Orenstein, E.C., Beijbom, O.,
Transfer Learning and Deep Feature Extraction for Planktonic Image Data Sets,
WACV17(1082-1088)
IEEE DOI 1609
Cameras, Computer vision, Data mining, Feature extraction, Oceans, Training BibRef

Riabchenko, E., Meissner, K., Ahmad, I., Iosifidis, A., Tirronen, V., Gabbouj, M., Kiranyaz, S.,
Learned vs. engineered features for fine-grained classification of aquatic macroinvertebrates,
ICPR16(2276-2281)
IEEE DOI 1705
Databases, Ecosystems, Feature extraction, Machine vision, Microscopy, Water, resources BibRef

Dai, J.L.[Jia-Lun], Yu, Z.B.[Zhi-Bin], Zheng, H.Y.[Hai-Yong], Zheng, B.[Bing], Wang, N.[Nan],
A Hybrid Convolutional Neural Network for Plankton Classification,
MCBMIIA16(III: 102-114).
Springer DOI 1704
BibRef

Raitoharju, J., Riabchenko, E., Meissner, K., Ahmad, I., Iosifidis, A., Gabbouj, M., Kiranyaz, S.,
Data Enrichment in Fine-Grained Classification of Aquatic Macroinvertebrates,
CVAUI16(43-48)
IEEE DOI 1701
Convolution BibRef

Hirata, N.S.T., Fernandez, M.A., Lopes, R.M.,
Plankton Image Classification Based on Multiple Segmentations,
CVAUI16(55-60)
IEEE DOI 1701
Algorithm design and analysis BibRef

Lee, H., Park, M., Kim, J.,
Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning,
ICIP16(3713-3717)
IEEE DOI 1610
Data models BibRef

Gao, G.M.[Guo-Ming], Liu, H.[Huan], Gu, Y.F.[Yan-Feng], Jia, X.P.[Xiu-Ping],
Normalized difference phytoplankton index (NDPI) and spatio-temporal cloud filtering for multitemporal cyanobacteria pollution analysis on Erie Lake in 2014,
MultiTemp15(1-4)
IEEE DOI 1511
lakes BibRef

Rahimi, A.M., Miller, R.J., Fedorov, D.V., Sunderrajan, S., Doheny, B.M., Page, H.M., Manjunath, B.S.,
Marine Biodiversity Classification Using Dropout Regularization,
CVAUI14(80-87)
IEEE DOI 1412
Biodiversity BibRef

Mazzei, L.[Luca], Marini, S.[Simone], Craig, J.[Jessica], Aguzzi, J.[Jacopo], Fanelli, E.[Emanuela], Priede, I.G.[Imants G.],
Automated Video Imaging System for Counting Deep-Sea Bioluminescence Organisms Events,
CVAUI14(57-64)
IEEE DOI 1412
Ash BibRef

Corgnati, L.[Lorenzo], Mazzei, L.[Luca], Marini, S.[Simone], Aliani, S.[Stefano], Conversi, A.[Alessandra], Griffa, A.[Annalisa], Isoppo, B.[Bruno], Ottaviani, E.[Ennio],
Automated Gelatinous Zooplankton Acquisition and Recognition,
CVAUI14(1-8)
IEEE DOI 1412
Feature extraction BibRef

Matuszewski, D.J.[Damian J.], Martins, C.I.O.[C. Iury O.], Cesar, Jr., R.M.[Roberto M.], Strickler, J.R.[J. Rudi], Lopes, R.M.[Rubens M.],
Visual rhythm-based plankton detection method for ballast water quality assessment,
ICIP12(1009-1012).
IEEE DOI 1302
BibRef

Zhao, F.[Feng], Lin, F.[Feng], Seah, H.S.[Hock Soon],
Bagging based plankton image classification,
ICIP09(2081-2084).
IEEE DOI 0911
BibRef

Alarcon, V.J., van der Zwaag, J., Moorhead, R.,
Estimation of Estuary Phytoplankton using a Web-based Tool for Visualization of Hyper-spectral Images,
AIPR06(25-25).
IEEE DOI 0610
BibRef

Alarcon, V.J., O'Hara, C.G.,
Advanced Techniques for Watershed Visualization,
AIPR06(30-30).
IEEE DOI 0610
BibRef

Luo, T.[Tong], Kramer, K., Samson, S., Remsen, A.,
Active learning to recognize multiple types of plankton,
ICPR04(III: 478-481).
IEEE DOI 0409
BibRef

Zhao, F.[Feng], Tang, X.[Xiaoou], Lin, F.[Feng], Samson, S., Remsen, A.,
Binary Plankton Image Classification Using Random Subspace,
ICIP05(I: 357-360).
IEEE DOI 0512
BibRef

Blaschko, M.B.[Matthew B.], Holness, G.[Gary], Mattar, M.A.[Marwan A.], Lisin, D.A.[Dimitri A.], Utgoff, P.E.[Paul E.], Hanson, A.R.[Allen R.], Schultz, H.[Howard], Riseman, E.M.[Edward M.], Sieracki, M.E.[Michael E.], Balch, W.M.[William M.], Tupper, B.[Ben],
Automatic In Situ Identification of Plankton,
WACV05(I: 79-86).
IEEE DOI 0502
BibRef

Lisin, D.A., Mattar, M.A., Blaschko, M.B., Learned-Miller, E.G., Benfield, M.C.,
Combining Local and Global Image Features for Object Class Recognition,
LCV05(III: 47-47).
IEEE DOI 0507
BibRef

Thonnat, M., Gandelin, M.,
An expert system for the automatic classification and description of zooplanktons from monocular images,
ICPR88(I: 114-118).
IEEE DOI 8811
BibRef

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Last update:Dec 7, 2017 at 17:23:10