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Summary: 3D Invariants with High Robustness to Local
Deformations for Automated Pollen Recognition
Olaf Ronneberger, Qing Wang, and Hans Burkhardt
Albert-Ludwigs-Universit¨at Freiburg, Institut f¨ur Informatik, Lehrstuhl f¨ur
Mustererkennung und Bildverarbeitung, Georges-K¨ohler-Allee Geb. 052,
79110 Freiburg, Deutschland
{ronneber,qwang,burkhardt}@informatik.uni-freiburg.de
Abstract. We present a new technique for the extraction of features
from 3D volumetric data sets based on group integration. The features
are invariant to translation, rotation and global radial deformations.
They are robust to local arbitrary deformations and nonlinear gray value
changes, but are still sensitive to fine structures. On a data set of 389 con-
focally scanned pollen from 26 species we get a precision/recall of 99.2%
with a simple 1NN classifier. On volumetric transmitted light data sets of
about 180,000 airborne particles, containing about 22,700 pollen grains
from 33 species, recorded with a low-cost optic in a fully automated
online pollen monitor the mean precision for allergenic pollen is 98.5%
(recall: 86.5%) and for the other pollen 97.5% (recall: 83.4%).
1 Introduction
Nearly all worldwide pollen forecasts are still based on manual counting of pollen
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