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A self-adaptive correction method for perspective distortions of image

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Abstract

Frequently, the shooting angles available to photograph an object are limited, and the resultant images contain perspective distortions. These distortions make more difficult to perform subsequent tasks like feature extraction and information identification. This paper suggested a perspective correction method that extracts automatically distortion features through edge detection. Results showed that this method is powerful in correcting images with perspective distortions. The corrected image has virtually little information missing, clear features and high recovery rate.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 61501150).

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Correspondence to Lihua Wu.

Additional information

Lihua Wu is a professor in School of Measurement and Communication at the Harbin University of Science and Technology. She obtained her PhD degree from Harbin University of Science and Technology, China in 2007. Her current research direction is signal and information processing.

Qinghua Shang is a professor in School of Electrical and Electronic Engineering at the Harbin University of Science and Technology. He obtained his BS degree from Harbin Shipbuilding Engineering Institute, China in 1982. His current research direction is signal and information processing.

Yupeng Sun obtained his master degree from Harbin University of Science and Technology, China in 2014. His current research direction is signal and information processing.

Xu Bai received the bachelor degree from the Department of Electronic and Information Engineering of Northwest Minzu University at Lanzhou, China in 2017. Currently he is pursuing the MA degree in School of Measurement and Communication, Harbin University of Science and Technology, China. His major is instrument science and technology.

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Wu, L., Shang, Q., Sun, Y. et al. A self-adaptive correction method for perspective distortions of image. Front. Comput. Sci. 13, 588–598 (2019). https://doi.org/10.1007/s11704-018-7269-8

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