20.7.3.7.6 Pollen Detection, Analysis

Chapter Contents (Back)
Pollen. Application, Pollen.

Carrión, P., Cernadas, E., Gálvez, J.F., Rodroguez-Damián, M., de Sá-Otero, P.,
Classification of honeybee pollen using a multiscale texture filtering scheme,
MVA(15), No. 4, October 2004, pp. 186-193.
Springer DOI 0410
BibRef

Rodriguez-Damian, M., Cernadas, E., Formella, A., Fernandez-Delgado, M., DeSa-Otero, P.,
Automatic Detection and Classification of Grains of Pollen Based on Shape and Texture,
SMC-C(36), No. 4, July 2006, pp. 531-542.
IEEE DOI 0606
BibRef

Rodroguez-Damian, M., Cernadas, E., de Sa-Otero, P., Formella, A.,
Pollen classification using brightness-based and shape-based descriptors,
ICPR04(II: 212-215).
IEEE DOI 0409
BibRef

Wang, Q.[Qing], Ronneberger, O.[Olaf], Burkhardt, H.[Hans],
Rotational Invariance Based on Fourier Analysis in Polar and Spherical Coordinates,
PAMI(31), No. 9, September 2009, pp. 1715-1722.
IEEE DOI 0907
BibRef
Earlier: A2, A1, A3:
3D Invariants with High Robustness to Local Deformations for Automated Pollen Recognition,
DAGM07(425-435).
Springer DOI 0709
BibRef

Ronneberger, O.[Olaf], Burkhardt, H.[Hans], Schultz, E.,
General-purpose object recognition in 3D volume data sets using gray-scale invariants: Classification of airborne pollen-grains recorded with a confocal laser scanning microscope,
ICPR02(II: 290-295).
IEEE DOI 0211
BibRef

Tambo, A.L.[Asongu L.], Bhanu, B.[Bir], Ung, N.[Nolan], Thakoor, N.[Ninad], Luo, N.[Nan], Yang, Z.B.[Zhen-Biao],
Understanding pollen tube growth dynamics using the Unscented Kalman 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


López-García, F.[Fernando], Valiente-González, J.M.[José Miguel], Escriche-Roberto, I.[Isabel], Juan-Borrás, M.[Marisol], Visquert-Fas, M.[Mario], Atienza-Vanacloig, V.[Vicente], Agustí-Melchor, M.[Manuel],
Classification of Honey Pollens with Imagenet Neural Networks,
CAIP23(II:192-200).
Springer DOI 2312
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

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Vegetables Detection, Vegetable Inspection, Roots, Tomatoes, Potatoes .


Last update:Mar 16, 2024 at 20:36:19