Automated Pollen Image Classification

Author

Nicholas Quentin HaasFollow

Date of Award

12-2011

Degree Type

Thesis

Degree Name

Master of Science

Major

Computer Science

Major Professor

J. Douglas Birdwell

Committee Members

Tse-Wei Wang, Roger Horn

Abstract

This Master of Science thesis reviews previous research, proposes a method anddemonstrates proof-of-concept software for the automated matching of pollen grainimages to satisfy degree requirements at the University of Tennessee. An ideal imagesegmentation algorithm and shape representation data structure is selected, alongwith a multi-phase shape matching system. The system is shown to be invariantto synthetic image translation, rotation, and to a lesser extent global contrast andintensity changes. The proof-of-concept software is used to demonstrate how pollengrains can be matched to images of other pollen grains, stored in a database, thatshare similar features with up to a 75% accuracy rate.

Recommended Citation

Haas, Nicholas Quentin, "Automated Pollen Image Classification. " Master's Thesis, University of Tennessee, 2011.
https://trace.tennessee.edu/utk_gradthes/1113