The generalization of the R-transform for invariant pattern representation

PATTERN RECOGNITION

卷:45

Article

Hoang, Thai V.^1,2  Tabbone, Salvatore¹ 

[1] LORIA, Bur B226, CNRS, UMR 7503, F-54506 Vandoeuvre Les Nancy, France

[2] MICA Ctr, HUST CNRS UMI 2954, Hanoi, Vietnam

关键词: Invariant pattern representation;    Radon transform;    R-transform;    R-signature;    Feature extraction;    Dominant directions;    Noise robustness;   

DOI  :  10.1016/j.patcog.2011.11.007

来源: Elsevier

【 摘 要 】

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. (C) 2011 Elsevier Ltd. All rights reserved.

【 授权许可】

Free