Vol.126, No.1, 2021, pp.379-396, doi:10.32604/cmes.2021.010771
OPEN ACCESS
ARTICLE
Fish-Eye Image Distortion Correction Based on Adaptive Partition Fitting
  • Yibin He1,2, Wenhao Xiong1, Hanxin Chen1,2,*, Yuchen Chen1, Qiaosen Dai1, Panpan Tu1, Gaorui Hu1
1 School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
2 Hubei Provincial Key Laboratory of Chemical Equipment, Intensification and Intrinsic Safety, Wuhan, 430205, China
* Corresponding Author: Hanxin Chen. Email: *
(This article belongs to this Special Issue: Security Enhancement of Image Recognition System in IoT based Smart Cities)
Received 28 March 2020; Accepted 18 May 2020; Issue published 22 December 2020
Abstract
The acquisition of images with a fish-eye lens can cause serious image distortion because of the short focal length of the lens. As a result, it is difficult to use the obtained image information. To make use of the effective information in the image, these distorted images must first be corrected into the perspective of projection images in accordance with the human eye’s observation abilities. To solve this problem, this study presents an adaptive classification fitting method for fish-eye image correction. The degree of distortion in the image is represented by the difference value of the distances from the distorted point and undistorted point to the center of the image. The target points selected in the image are classified by the difference value. In the areas classified by different distortion differences, different parameter curves were used for fitting and correction. The algorithm was verified through experiments. The results showed that this method has a substantial correction effect on fish-eye images taken by different fish-eye lenses.
Keywords
Fish-eye lens; image distortion; distortion difference; adaptive partition fitting
Cite This Article
He, Y., Xiong, W., Chen, H., Chen, Y., Dai, Q. et al. (2021). Fish-Eye Image Distortion Correction Based on Adaptive Partition Fitting. CMES-Computer Modeling in Engineering & Sciences, 126(1), 379–396.
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