The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Kernels for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Using irreducible group representations for invariant 3d shape description
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
A general framework for quadratic Volterra filters for edge enhancement
IEEE Transactions on Image Processing
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It is well known that linear filters are not powerful enough for many low-level image processing tasks. But it is also very difficult to design robust non-linear filters that respond exclusively to features of interest and that are at the same time equivariant with respect to translation and rotation. This paper proposes a new class of rotation-equivariant non-linear filters that is based on the principle of group integration. These filters become efficiently computable by an iterative scheme based on repeated differentiation of products and summations of the intermediate results. Our experiments show that the proposed filter detects pollen porates with only half as many errors than alternative approaches, when high localization accuracy is required.