Two-dimensional, model-based, boundary matching using footprints
International Journal of Robotics Research
3d object recognition using invariant feature indexing of interpretation tables
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
Beyond uniformity and independence: analysis of R-trees using the concept of fractal dimension
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Searching in Parallel for Similar Strings
IEEE Computational Science & Engineering
A Bayesian approach to model matching with geometric hashing
Computer Vision and Image Understanding
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Multidimensional Indexing for Recognizing Visual Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Managing Statistical Behavior of Large Data Sets in Shared-Nothing Architectures
IEEE Transactions on Parallel and Distributed Systems
Affine Invariant Pattern Recognition Using Multiscale Autoconvolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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]脳[0,1]脳[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.