Paper
22 October 2010 Subpixel edge-detection algorithm based on pseudo-Zernike moments
Kun Zhang, Haiqing Chen, Qingwen Liang, Chong Huang, Jiakun Xu
Author Affiliations +
Abstract
In order to meet the demands of high precision localization and anti-interference performance for optoelectronic detection and imaging devices, a new subpixel edge detection approach based on orthogonal Pseudo-Zernike moments is proposed in this paper with both theoretical analysis and experimental demonstration. First, the ideal step model of subpixel edge is established, and the specific characteristics of edge points can be extracted through the convolution with each order of Pseudo-Zernike moments. According to the principle of amplitude rotation invariance, the parameters of subpixel edge can be obtained by analyzing the relationships between different orders and repetitions of Pseudo-Zernike moments when the image edge is rotated to the vertical direction. And then the actual coordination of the subpixel edge point can be identified. Comparing with other approaches such as spatial moment operators, and Zernike moment algorithm, the experiment results prove that the proposed method has the virtue of higher measuring precision and better noise suppression performance. The edge detection accuracy is up to 0.07 pixel for straight lines with noise, and 0.1 pixel for curves with noise. Therefore, it can be concluded that the proposed method is an efficient approach with a relatively high accuracy and stabilization for image edge detection.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kun Zhang, Haiqing Chen, Qingwen Liang, Chong Huang, and Jiakun Xu "Subpixel edge-detection algorithm based on pseudo-Zernike moments", Proc. SPIE 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology, 76584X (22 October 2010); https://doi.org/10.1117/12.866023
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KEYWORDS
Edge detection

Detection and tracking algorithms

Optoelectronics

Algorithm development

Convolution

Image processing

Imaging devices

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