Natural Representations for Straight Lines and the Hough Transform on Discrete Arrays
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-accuracy rotation of images
CVGIP: Graphical Models and Image Processing
Image Representation Via a Finite Radon Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
DCGI '99 Proceedings of the 8th International Conference on Discrete Geometry for Computer Imagery
Sampling properties of the discrete radon transform
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
An exact, non-iterative Mojette inversion technique utilising ghosts
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Computing admissible rotation angles from rotated digital images
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Exact, scaled image rotation using the finite radon transform
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
Quasi-affine transformation in higher dimension
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
Incremental and transitive discrete rotations
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
Growth of discrete projection ghosts created by iteration
DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
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The Finite Radon Transform (FRT) is a discrete analogue of classical tomography. The FRT permits exact reconstruction of a discrete object from its discrete projections. The set of projection angles for the FRT is intrinsic to each image array size. It is shown here that the set of FRT angles is closed under a rotation by any of its members. A periodic re-ordering of the elements of the 1D FRT projections is then equivalent to an exact 2D image rotation. FRT-based rotations require minimal interpolation and preserve all of the original image pixel intensities. This approach has applications in image feature matching, multi-scale data representation and data encryption.