Fast and stable algorithms for high-order Pseudo Zernike moments and image reconstruction

Deng, An-Wen; Gwo, Chih-Ying

doi:10.1016/j.amc.2018.04.001

Cited by 9 publications

(9 citation statements)

References 25 publications

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“…Deng and Gwo also proposed a recursive algorithm to compute pseudo-Zernike moments in 2018 [10]. In their work, the ultimate recursive relations among the radial polynomials R nm (ρ) and the intermediate values noted as…”

Section: Recursive Methodsmentioning

confidence: 99%

“…3.1(c). Deng's recursive methods are recently proposed and perform better than the q-recursive method [9,10]. Therefore we have chosen them and applied the two optimization methods shown in Section 3.2 in our GPU implementation.…”

Section: Recursive Methodsmentioning

confidence: 99%

“…To compute the Zernike moments A nm in a Cartesian coordinate system, a commonly used format iŝ 10) where ∆x and ∆y are the sampling intervals in the x and y directions [21], which are 2 N .…”

Section: Zernike Momentsmentioning

confidence: 99%

“…The times increase when T or k increases. The comparison result of pseudo-Zernike moments computation times between Deng's recursive method [10] and ours is shown in Table 5.8.…”

Section: Computational Efficiencymentioning

confidence: 99%

“…The Zernike and pseudo-Zernike kernel functions include both radial and exponential polynomials, and the former involves a series of factorials, which makes it a time-consuming process to calculate. The computational accuracy is also an important issue for developing applications based on Zernike or pseudo-Zernike moments, while some researchers have made a good effort to find solutions in the past a few years [2,[6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Accelerating Computation of Zernike and Pseudo-Zernike Moments with a GPU Algorithm

Li¹

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Although Zernike and pseudo-Zernike moments have some advanced properties, the computation process is generally very time-consuming, which has limited their practical applications. To improve the computational efficiency of Zernike and pseudo-Zernike moments, in this research, we have explored the use of GPU to accelerate moments computation, and proposed a GPUaccelerated algorithm. The newly developed algorithm is implemented in Python and CUDA C++ with optimizations based on symmetric properties and k × k sub-region scheme. The experimental results are encouraging and have shown that our GPU-accelerated algorithm is able to compute Zernike moments up to order 700 for an image sized at 512 × 512 in 1.7 seconds and compute pseudo-Zernike moments in 3.1 seconds. We have also verified the accuracy of our GPU algorithm by performing image reconstructions from the higher orders of Zernike and pseudo-Zernike moments. For an image sized at 512 × 512, with the maximum order of 700 and k = 11, the PSNR (Peak Signal to Noise Ratio) values of its reconstructed versions from Zernike and pseudo-Zernike moments are 44.52 and 46.29 separately. We have performed image reconstructions from partial sets of Zernike and pseudo-Zernike moments with various order n and different repetition m. Experimental results of both Zernike and pseudo-Zernike moments show that the images reconstructed from the moments of lower and higher orders preserve the principle contents and details of the original image respectively, while moments of positive and negative m result in identical images. Lastly, we have proposed a set of feature vectors based on pseudo-Zernike moments for Chinese character recognition. Three different feature vectors are composed of different parts of four selected lower pseudo-Zernike moments. Experiments on a set of 6,762 Chinese characters show that this method performs well to recognize similar-shaped Chinese characters.

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Section: Recursive Methodsmentioning

confidence: 99%

Section: Recursive Methodsmentioning

confidence: 99%

Section: Zernike Momentsmentioning

confidence: 99%

“…The times increase when T or k increases. The comparison result of pseudo-Zernike moments computation times between Deng's recursive method [10] and ours is shown in Table 5.8.…”

Section: Computational Efficiencymentioning

confidence: 99%