A self-adaptive correction method for perspective distortions of image

Lihua WU, Qinghua SHANG, Yupeng SUN, Xu BAI

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PDF(765 KB)
Front. Comput. Sci. ›› 2019, Vol. 13 ›› Issue (3) : 588-598. DOI: 10.1007/s11704-018-7269-8
RESEARCH ARTICLE

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.

Keywords

perspective correction / perspective transformation / Hough transform / edge detection

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Lihua WU, Qinghua SHANG, Yupeng SUN, Xu BAI. A self-adaptive correction method for perspective distortions of image. Front. Comput. Sci., 2019, 13(3): 588‒598 https://doi.org/10.1007/s11704-018-7269-8

References

[1]
Agah A, Gerhard W. Applied artificial intelligence techniques for identifying the lazy eye vision disorder. Journal of Intelligent Systems, 2011, 20(2): 101–127
[2]
Huang Y, Zhao S. License plate localization based on machine vision. Advanced Materials Research, 2011, 1453(339): 64–73
CrossRef Google scholar
[3]
Zhang J. Research on the geometric distortion auto-correction algorithm for image scanned. Applied Mechanics and Materials, 2014, 3468(644): 30–44
CrossRef Google scholar
[4]
Kolecki J, Rzonca A. Accuracy analysis of automatic distortion correction. Geodesy and Cartography, 2015, 64(1): 3–14
CrossRef Google scholar
[5]
Shen C, Zhang J. The fast correction algorithm for inclined license plate recognition. Computer Engineering, 2004, 30(13): 122–124
[6]
Kasaei S, Monadjemi S. A new method of license plate recognition. American Journal of Applied Sciences, 2009, 6(12): 46–52
CrossRef Google scholar
[7]
Gao F, Wen G. Affine invariant feature extraction using affine geometry. Journal of Image and Graphics, 2011, 16(3): 389–397
[8]
Hindman N, Moshesh I. Image partition regularity of affine transformations. Journal of Combinatorial Theory, 2007, 114(8): 51–53
[9]
Wirtz D, Paulus K. Model-based recognition of 2D objects under perspective distortion. Pattern Recognition and Image Analysis, 2012, 22(3): 72–79
CrossRef Google scholar
[10]
Ecabert O, Thiran J. Adaptive Hough transform for the detection of natural shapes under weak affine transformations. Pattern Recognition Letters, 2004, 25(12): 6–16
CrossRef Google scholar
[11]
Duan R, Zhao W. A fast line detection algorithm based on improved Hough transforms. Chinese Journal of Scientific Instrument, 2010, 31(12): 75–81
[12]
Wang C. Fast line extraction algorithm based on improved Hough transformation. Advanced Materials Research, 2014, 3181(926): 97–104
CrossRef Google scholar
[13]
Boukharouba N. A new algorithm for skew correction and baseline detection based on the randomized Hough transform. Journal of King Saud University-Computer and Information Sciences, 2017, 29(1): 29–38
CrossRef Google scholar
[14]
Chen Y, Yang Y. Two improved algorithms based on Huff transform elliptic detection. Semiconductor Optoelectronics, 2017, 38(5): 745–750
[15]
Bieniecki W. Identification and assessment of selected handwritten function graphs using least square approximation combined with general Hough transform. Image Processing & Communications, 2017, 22(4): 23–42
CrossRef Google scholar
[16]
Raman M, Aggarwal H. Study and comparison of various image edge detection techniques. International Journal of Image Processing, 2009, 3(1): 132–138
[17]
Zareizadeh Z, Reza P. A recursive color image edge detection method using green’s function approach. International Journal for Light and Electron Optics, 2013, 124(21): 37–40
CrossRef Google scholar
[18]
Puzio L. Adaptive edge detection method for images. Electronics Review, 2008, 16(1): 3–22
[19]
Yang Y, Wei X. Image interpolation algorithm based on edge features. Applied Mechanics and Materials, 2011, 1156(50): 8–15
CrossRef Google scholar
[20]
Ren W L, Zhu Z P. A convergence relation between discrete and continuous regular quaternionic functions. Advances in Applied Clifford Algebras, 2017, 27(2): 1715–1740
CrossRef Google scholar
[21]
Slavik A. Discrete bessel functions and partial difference equations. Journal of Difference Equations and Applications, 2018, 24(3): 425–437
CrossRef Google scholar
[22]
Frank S. Taylor series expansion in discrete clifford analysis. Complex Analysis and Operator Theory, 2014, 8(2): 485–511
CrossRef Google scholar
[23]
Yong H, Qing C. Application of image analysis based on canny operator edge detection algorithm in measuring railway out-of-gauge goods. Advanced Materials Research, 2014, 3137(912): 1172–1176
[24]
Hou Z, Wei G. A new approach to edge detection. Pattern Recognition, 2002, 35(7): 406–408
CrossRef Google scholar
[25]
Tian X. A novel image edge detection algorithm based on Prewitt operator and wavelet transform. International Journal of Advancements in Computing Technology, 2012, 4(19): 73–82
[26]
Shen D, Zhang L. Application of improved Sobel algorithm in image edge detection. Applied Mechanics and Materials, 2014, 3561(678): 7–17
CrossRef Google scholar
[27]
Wang Z, Wang K, Yang F. Image segmentation of overlapping leaves based on chan-vese model and Sobel operator. Information Processing in Agriculture, 2018, 5(1): 1–10
CrossRef Google scholar
[28]
Wang L, Kang K. Research and analysis of edge-detection of digital images. Applied Mechanics and Materials, 2013, 2171(263): 43–50
[29]
Akram A, Ziad A. A practical approach of selecting the edge detector parameters to achieve a good edge map of the gray image. Journal of Computer Science, 2009, 5(5): 26–32
[30]
Mcnamara A. Visual perception in realistic image synthesis. Computer Graphics Forum, 2001, 20(4): 211–224
CrossRef Google scholar
[31]
Aboura K, Hmouz R. An overview of image analysis algorithms for license plate recognition. Organizacija, 2017, 50(3): 285–295
CrossRef Google scholar
[32]
Li X, Bai W. Method for rectifying image deviation based on perspective transformation. Materials Science and Engineering, 2017, 231(1): 12–21
[33]
Moutakki Z, Mohamed O, Afdel K. Real-time system based on feature extraction for vehicle detection and classification. Transport and Telecommunication Journal, 2018, 19(2): 93–102
CrossRef Google scholar
[34]
Tian W. Image correction and restoration algorithm based on Hough line detection and 2D perspective transformation. Electron Measurement Technology, 2017, 40(9): 128–131
[35]
Gennadyevich S, Nikolaevich B. Algorithm of correction of error caused by perspective distortions of measuring mark images. Mechanics & Industry, 2016, 17(7): 713–719
CrossRef Google scholar

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2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
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