Automatic Pollen Classification and Segmentation Using U-Nets and Synthetic Data

Author:

Boldeanu Mihai1ORCID,Gonzalez-Alonso Monica2ORCID,Cucu Horia1ORCID,Burileanu Corneliu1ORCID,Maya-Manzano Jose Maria3ORCID,Buters Jeroen Titus Maria3

Affiliation:

1. Speech and Dialogue Research Laboratory, Politehnica University of Bucharest, Bucharest, Romania

2. Environmental Biology Department, University of Navarra, Pamplona, Spain

3. Center of Allergy and Environment (ZAUM) and Helmholtz Center, Technical University of Munich, Munich, Germany

Funder

Grant of the Romanian Ministry of Education and Research, CCCDI—UEFISCDI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference41 articles.

1. Batch normalization: Accelerating deep network training by reducing internal covariate shift;ioffe;Proc Int Conf Mach Learn,2015

2. Deep learning using rectified linear units (ReLU);agarap;arXiv 1803 08375,2018

3. Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

4. Land cover classification from satellite imagery with U-Net and Lovász-softmax loss;rakhlin;Proc IEEE/CVF Conf Comput Vis Pattern Recognit Workshops (CVPRW),2018

5. Microscopy cell nuclei segmentation with enhanced U-Net

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