Article ID Journal Published Year Pages File Type
530297 Pattern Recognition 2012 19 Pages PDF
Abstract

The beneficial properties of the Radon transform make it a useful intermediate representation for the extraction of invariant features from pattern images for the purpose of indexing/matching. This paper revisits the problem of Radon image utilization with a generic view on a popular Radon transform-based transform and pattern descriptor, the R-transform and R-signature, bringing in a class of transforms and descriptors spatially describing patterns at all directions and at different levels, while maintaining the beneficial properties of the conventional R-transform and R-signature. The domain of this class, which is delimited due to the existence of singularities and the effect of sampling/quantization and additive noise, is examined. Moreover, the ability of the generic R-transform to encode the dominant directions of patterns is also discussed, adding to the robustness to additive noise of the generic R-signature. The stability of dominant direction encoding by the generic R-transform and the superiority of the generic R-signature over existing invariant pattern descriptors on grayscale and binary noisy datasets have been confirmed by experiments.

► We generalize the R-transform/R-signature for pattern description/recognition. ► The generalization maintains beneficial properties of the conventional one. ► The domain of this generalization is discussed. ► The discrimination power and sensitivity to noise are investigated. ► The generic R-signature has superior performance to comparison methods.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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