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Legendre moment invariants to blur and affine transformation and their use in image recognition |
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| Authors: | Xiubin Dai Hui Zhang Tianliang Liu Huazhong Shu Limin Luo |
| Affiliation: | 1. School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, 210046, Nanjing, China 2. Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, 210096, Nanjing, China 3. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, 210003, Nanjing, China |
| Abstract: | The processing of the images simultaneously degraded by blur and affine transformation has become a key task in many applications and many novel methods are designed specifically for it in which the moment-based methods play an important role. However, the existing moment-based methods all resort to non-orthogonal moments invariants which have problem of information redundancy and are sensitive to noise. In this paper, we construct a new set of combined invariants of orthogonal Legendre moments which hold for blur and affine transformation together. The experimental results show that the proposed invariants have better discriminative power and robustness to noise with the comparison to other invariants. |
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