Invariant Features for Gray Scale Images
Mustererkennung 1995, 17. DAGM-Symposium
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Invariance via group-integration: a feature framework for 3D biomedical image analysis
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
A fast and reliable coin recognition system
Proceedings of the 29th DAGM conference on Pattern recognition
3D invariants with high robustness to local deformations for automated pollen recognition
Proceedings of the 29th DAGM conference on Pattern recognition
3D object detection using a fast voxel-wise local spherical Fourier tensor transformation
Proceedings of the 32nd DAGM conference on Pattern recognition
Semi-supervised learning of edge filters for volumetric image segmentation
Proceedings of the 32nd DAGM conference on Pattern recognition
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Since biology and medicine apply increasingly fast volumetric imaging techniques and aim at extracting quantitative data from these images, the need for efficient image analysis techniques like detection and classification of 3D structures is obvious. A common approach is to extract local features, e.g. group integration has been used to gain invariance against rotation and translation. We extend these group integration features by including vectorial information and spherical harmonics descriptors. From our vectorial invariants we derive a very robust detector for spherical structures in low-quality images and show that it can be computed very fast. We apply these new invariants to 3D confocal laser-scanning microscope images of the Arabidopsis root tip and extract position and type of the cell nuclei. Then it is possible to build a biologically relevant, architectural model of the root tip.