J Knapman
Image and Vision Computing
We derive and discuss a set of parametric equations which, when given a convex 3D feature domain, K, will generate affine invariants with the property that the invariants' values are uniformly distributed in the region [0,1]×[0,1]×[0,1]. Once the shape of the feature domain K is determined and fixed it is straightforward to compute the values of the parameters and thus the proposed scheme can be tuned to a specific feature domain. The features of all recognizable objects (models) are assumed to be three-dimensional points and uniformly distributed over K. The scheme leads to improved discrimination power, improved computational-load and storage-load balancing and can also be used to determine and identify biases in the database of recognizable models (over-represented constructs of object points). Obvious enhancements produce rigid-transformation and similarity-transformation invariants with the same good distribution properties, making this approach generally applicable.
J Knapman
Image and Vision Computing
Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
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CSCCVPR 1998
Hagen Soltau, Lidia Mangu, et al.
ASRU 2011