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0410
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Rodriguez-Damian, M.,
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Automatic Detection and Classification of Grains of Pollen Based on
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SMC-C(36), No. 4, July 2006, pp. 531-542.
IEEE DOI
0606
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Rodroguez-Damian, M.,
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0409
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Rotational Invariance Based on Fourier Analysis in Polar and Spherical
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PAMI(31), No. 9, September 2009, pp. 1715-1722.
IEEE DOI
0907
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Earlier: A2, A1, A3:
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Springer DOI
0709
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Ronneberger, O.[Olaf],
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Schultz, E.,
General-purpose object recognition in 3D volume data sets using
gray-scale invariants: Classification of airborne pollen-grains recorded
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Tambo, A.L.[Asongu L.],
Bhanu, B.[Bir],
Ung, N.[Nolan],
Thakoor, N.[Ninad],
Luo, N.[Nan],
Yang, Z.B.[Zhen-Biao],
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Filter,
PRL(72), No. 1, 2016, pp. 100-108.
Elsevier DOI
1604
Pollen tubes
BibRef
Tambo, A.L.[Asongu L.],
Bhanu, B.[Bir],
Luo, N.[Nan],
Harlowt, G.[Geoffrey],
Yang, Z.B.[Zhen-Biao],
Integrated Model for Understanding Pollen Tube Growth in Video,
ICPR14(2727-2732)
IEEE DOI
1412
BibRef
Tambo, A.L.[Asongu L.],
Bhanu, B.[Bir],
Dynamic bi-modal fusion of images for the segmentation of pollen
tubes in video,
ICIP15(148-152)
IEEE DOI
1512
Fluorescence and Brightfield video analysis
BibRef
Saito, Y.[Yasunori],
Ichihara, K.[Kentaro],
Morishita, K.[Kenzo],
Uchiyama, K.[Kentaro],
Kobayashi, F.[Fumitoshi],
Tomida, T.[Takayuki],
Remote Detection of the Fluorescence Spectrum of Natural Pollens
Floating in the Atmosphere Using a Laser-Induced-Fluorescence
Spectrum (LIFS) Lidar,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link
1811
BibRef
Cholleton, D.[Danaël],
Rairoux, P.[Patrick],
Miffre, A.[Alain],
Laboratory Evaluation of the (355, 532) nm Particle Depolarization
Ratio of Pure Pollen at 180.0° Lidar Backscattering Angle,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Ratnayake, M.N.[Malika Nisal],
Amarathunga, D.C.[Don Chathurika],
Zaman, A.[Asaduz],
Dyer, A.G.[Adrian G.],
Dorin, A.[Alan],
Spatial Monitoring and Insect Behavioural Analysis Using Computer
Vision for Precision Pollination,
IJCV(131), No. 3, March 2023, pp. 591-606.
Springer DOI
2302
BibRef
And:
Correction:
IJCV(131), No. 5, May 2023, pp. 1300-1301.
Springer DOI
2305
BibRef
Earlier: A1, A4, A5, Only:
Towards Computer Vision and Deep Learning Facilitated Pollination
Monitoring for Agriculture,
AgriVision21(2915-2924)
IEEE DOI
2109
Deep learning, Visualization, Insects, Pipelines,
Production, Agriculture
BibRef
Khanzhina, N.[Natalia],
Kashirin, M.[Maxim],
Filchenkov, A.[Andrey],
New Bayesian Focal Loss Targeting Aleatoric Uncertainty Estimate:
Pollen Image Recognition,
CVMI23(4253-4262)
IEEE DOI
2309
BibRef
Yang, N.[Nana],
Joos, V.[Victor],
Jacquemart, A.L.,
Buyens, C.[Christel],
de Vleeschouwer, C.,
Using Pure Pollen Species When Training a CNN to Segment Pollen
Mixtures,
AgriVision22(1694-1703)
IEEE DOI
2210
Training, Image segmentation, Costs, Computational modeling,
Microscopy, Pattern recognition
BibRef
Mahbod, A.[Amirreza],
Schaefer, G.[Gerald],
Ecker, R.[Rupert],
Ellinger, I.[Isabella],
Pollen Grain Microscopic Image Classification Using an Ensemble of
Fine-tuned Deep Convolutional Neural Networks,
AIHA20(344-356).
Springer DOI
2103
BibRef
Battiato, S.[Sebastiano],
Guarnera, F.[Francesco],
Ortis, A.[Alessandro],
Trenta, F.[Francesca],
Ascari, L.[Lorenzo],
Siniscalco, C.[Consolata],
de Gregorio, T.[Tommaso],
Suárez, E.[Eloy],
Pollen Grain Classification Challenge 2020,
Pollen20(469-479).
Springer DOI
2103
BibRef
Fang, C.[Chao],
Hu, Y.[Yutao],
Zhang, B.C.[Bao-Chang],
Doermann, D.[David],
The Fusion of Neural Architecture Search and Destruction and
Construction Learning,
Pollen20(480-489).
Springer DOI
2103
BibRef
Gui, P.H.[Peng-Hui],
Wang, R.W.[Ruo-Wei],
Zhu, Z.B.[Zheng-Bang],
Zhu, F.Y.[Fei-Yu],
Zhao, Q.J.[Qi-Jun],
Improved Data Augmentation of Deep Convolutional Neural Network for
Pollen Grains Classification,
Pollen20(490-500).
Springer DOI
2103
BibRef
Battiato, S.,
Ortis, A.,
Trenta, F.,
Ascari, L.,
Politi, M.,
Siniscalco, C.,
POLLEN13K: A Large Scale Microscope Pollen Grain Image Dataset,
ICIP20(2456-2460)
IEEE DOI
2011
Image segmentation, Pipelines, Image color analysis, Microscopy,
Support vector machines, Task analysis, Machine learning,
aerobiology
BibRef
Trenta, F.[Francesca],
Ortis, A.[Alessandro],
Battiato, S.[Sebastiano],
Fine-Grained Image Classification for Pollen Grain Microscope Images,
CAIP21(I:341-351).
Springer DOI
2112
BibRef
Battiato, S.[Sebastiano],
Ortis, A.[Alessandro],
Trenta, F.[Francesca],
Ascari, L.,
Politi, M.,
Siniscalco, C.,
Detection and Classification of Pollen Grain Microscope Images,
Microscopy20(4220-4227)
IEEE DOI
2008
Pipelines, Image color analysis, Microscopy, Task analysis,
Machine learning, Image segmentation, Feature extraction
BibRef
Yang, C.,
Collins, J.,
Deep Learning for Pollen Sac Detection and Measurement on Honeybee
Monitoring Video,
IVCNZ19(1-6)
IEEE DOI
2004
biology computing, convolutional neural nets,
learning (artificial intelligence), object detection,
deep learning
BibRef
Pedersen, B.,
Bailey, D.G.,
Hodgson, R.M.,
Holt, K.,
Marsland, S.,
Model and feature selection for the classification of dark field
pollen images using the classifynder system,
IVCNZ17(1-5)
IEEE DOI
1902
feature extraction, feature selection, image classification,
feature selection, classifynder system, SURF features,
Bag of Visual Words1
BibRef
Frejlichowski, D.[Dariusz],
Detection of Pollen Grains in Digital Microscopy Images by Means of
Modified Histogram Thresholding,
ICCVG18(308-315).
Springer DOI
1810
BibRef
Amu, G.,
Hasi, S.,
Digital description and recognition of pollen granules with invariant
moments,
ICIVC17(268-271)
IEEE DOI
1708
Image recognition, Microscopy, Microstructure, Pattern recognition,
Shape, Testing, Training, image recognition, invariant moment,
microscopic image, pollen, granule
BibRef
Filipovych, R.,
Daood, A.,
Ribeiro, E.,
Bush, M.,
Pollen recognition in optical microscopy by matching multifocal image
sequences,
ICPR16(2127-2132)
IEEE DOI
1705
Histograms, Image segmentation, Image sequences,
Mathematical model, Microscopy, Optical microscopy, Visualization
BibRef
Daood, A.,
Ribeiro, E.,
Bush, M.,
Pollen recognition using a multi-layer hierarchical classifier,
ICPR16(3091-3096)
IEEE DOI
1705
Convolution, Feature extraction, Fractals, Histograms,
Support vector machines, Training, Training, data
BibRef
Tambo, A.L.,
Bhanu, B.,
Temporal dynamics of tip fluorescence predict cell growth behavior in
pollen tubes,
ICPR16(1171-1176)
IEEE DOI
1705
Electron tubes, Feature extraction, Fluorescence,
Mathematical model, Oscillators, Shape, Turning, tip growth,
tip growth classification, tip growth cycle detection, tip, growth, features
BibRef
Daood, A.[Amar],
Ribeiro, E.[Eraldo],
Bush, M.[Mark],
Classifying Pollen Using Robust Sequence Alignment of Sparse Z-Stack
Volumes,
ISVC16(I: 331-340).
Springer DOI
1701
BibRef
Daood, A.[Amar],
Ribeiro, E.[Eraldo],
Bush, M.[Mark],
Pollen Grain Recognition Using Deep Learning,
ISVC16(I: 321-330).
Springer DOI
1701
BibRef
Kong, S.[Shu],
Punyasena, S.,
Fowlkes, C.C.[Charless C.],
Spatially Aware Dictionary Learning and Coding for Fossil Pollen
Identification,
Microscopy16(1305-1314)
IEEE DOI
1612
BibRef
Lozano-Vega, G.[Gildardo],
Benezeth, Y.[Yannick],
Classification of Pollen Apertures Using Bag of Words,
CIAP13(I:712-721).
Springer DOI
1311
BibRef
Nguyen, N.R.,
Donalson-Matasci, M.,
Shin, M.C.,
Improving pollen classification with less training effort,
WACV13(421-426).
IEEE DOI
1303
BibRef
Boucher, A.[Alain],
Thonnat, M.[Monique],
Object recognition from 3d blurred images,
ICPR02(I: 800-803).
IEEE DOI
0211
Microscope images of pollen grains. Classify based on
learned features.
BibRef
France, I.,
Duller, A.W.G.,
Lamb, H.F.,
Duller, G.A.T.,
A comparative study of approaches to automatic pollen identification,
BMVC97(xx-yy).
HTML Version.
0209
BibRef
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