Principles and methods for automated palynology

Semi-automated segmentation of pollen grains in microscopic images: a tool for three imaging modes

(2013) Stefan Johnsrud et al.   GRANA

Classifying black and white spruce pollen using layered machine learning

(2012) Surangi W. Punyasena et al.   NEW PHYTOLOGIST

Capturing the Surface Texture and Shape of Pollen: A Comparison of Microscopy Techniques

(2012) Mayandi Sivaguru et al.   PLoS One

An expert classification system of pollen of Onopordum using a rough set approach

(2012) Yılmaz Kaya et al.   REVIEW OF PALAEOBOTANY AND PALYNOLOGY

Recent warming by latitude associated with increased length of ragweed pollen season in central North America

(2011) L. Ziska et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Progress towards an automated trainable pollen location and classifier system for use in the palynology laboratory

(2011) K. Holt et al.   REVIEW OF PALAEOBOTANY AND PALYNOLOGY

Does climate affect pollen morphology? Optimal size and shape of pollen grains under various desiccation intensity

(2011) Maciej Jan Ejsmond et al.   Ecosphere

Pollen characterization and identification by elastically scattered light

(2010) Mario Surbek et al.   Journal of Biophotonics

Time to automate identification

(2010) Norman MacLeod et al.   NATURE

Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning

(2009) R. Dell’Anna et al.   ANALYTICAL AND BIOANALYTICAL CHEMISTRY

Counting pollen grains using readily available, free image processing and analysis software

(2009) Clayton M. Costa et al.   ANNALS OF BOTANY

Classification of pollen species using autofluorescence image analysis

(2009) Kotaro Mitsumoto et al.   JOURNAL OF BIOSCIENCE AND BIOENGINEERING