Deformation Models for Image Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant features for searching in protein fold databases
International Journal of Computer Mathematics - Bioinformatics
Technical section: Second order 3D shape features: An exhaustive study
Computers and Graphics
3D invariants with high robustness to local deformations for automated pollen recognition
Proceedings of the 29th DAGM conference on Pattern recognition
Invariance in kernel methods by haar-integration kernels
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Shape retrieval and recognition based on fuzzy histogram
Journal of Visual Communication and Image Representation
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A very common type of a-priori knowledge in pattern analysis problems is invariance of the input data with respect to transformation groups, e.g. geometric transformations of image data like shifting, scaling etc. For enabling most general analysis techniques, this knowledge should be incorporated in the feature-extraction stage. In the present work a method for this, called Haar-integration, is generalized to make it applicable to more general transformation sets, namely subsets of transformation groups. The resulting features are no longer precisely invariant, but their variability can be adjusted and quantified. Experimental results demonstrate the increased separability by these features and considerably improved recognition performance on a character recognition task.