3D invariants with high robustness to local deformations for automated pollen recognition

Authors:
Olaf Ronneberger;Qing Wang;Hans Burkhardt
Affiliations:
Albert-Ludwigs-Universität Freiburg, Institut für Informatik, Lehrstuhl für Mustererkennung und Bildverarbeitung, Freiburg, Deutschland;Albert-Ludwigs-Universität Freiburg, Institut für Informatik, Lehrstuhl für Mustererkennung und Bildverarbeitung, Freiburg, Deutschland;Albert-Ludwigs-Universität Freiburg, Institut für Informatik, Lehrstuhl für Mustererkennung und Bildverarbeitung, Freiburg, Deutschland
Venue:
Proceedings of the 29th DAGM conference on Pattern recognition
Year:
2007

Citing 8
Cited 3

Invariant Features for Gray Scale Images

Mustererkennung 1995, 17. DAGM-Symposium
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adjustable Invariant Features by Partial Haar-Integration

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Invariant Features for 3D-Data based on Group Integration using Directional Information and Spherical Harmonic Expansion

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Automatic recognition of biological particles in microscopic images

Pattern Recognition Letters
Fast scalar and vectorial grayscale based invariant features for 3d cell nuclei localization and classification

DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Voxel-wise gray scale invariants for simultaneous segmentation and classification

PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Snakes, shapes, and gradient vector flow

IEEE Transactions on Image Processing

Increasing the Dimension of Creativity in Rotation Invariant Feature Design Using 3D Tensorial Harmonics

Proceedings of the 31st DAGM Symposium on Pattern Recognition
SHOG: spherical HOG descriptors for rotation invariant 3D object detection

DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

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 confocally 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%).