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research-article

A camera model for cameras with hypercentric lenses and some example applications

Authors:

Carsten StegerAuthors Info & Claims

Volume 30, Issue 6

Pages 1013 - 1028

https://doi.org/10.1007/s00138-019-01032-w

Published: 01 September 2019 Publication History

Abstract

We propose a camera model for cameras with hypercentric lenses. Because of their geometry, hypercentric lenses allow to image the top and the sides of an object simultaneously. This makes them useful for certain inspections tasks, for which otherwise multiple images would have to be acquired and stitched together. After describing the projection geometry of hypercentric lenses, we derive a camera model for hypercentric lenses that is intuitive for the user. Furthermore, we describe how to determine the parameter values of the model by calibrating the camera with a planar calibration object. We also apply our camera model to two example applications: in the first application, we show how two cameras with hypercentric lenses can be used for dense 3D reconstruction. For an efficient reconstruction, the images are rectified such that corresponding points occur in the same image row. Standard rectification methods would result in perspective distortions in the images that would prevent stereo matching algorithms from robustly establishing correspondences. Therefore, we propose a new rectification method for objects that are approximately cylindrical in shape, which enables a robust and efficient reconstruction. In the second application, we show how to unwrap cylindrical objects to simplify further inspection tasks. For the unwrapping, the pose of the cylinder must be known. We show how to determine the pose of the cylinder based on a single camera image and based on two images of a stereo camera setup.

References

[1]

Batchelor, B.G.: Machine vision for industrial applications. In: Batchelor, B.G. (ed.) Machine Vision Handbook, pp. 1–59. Springer, London (2012)

[2]

Beyerer, J., Puente León, F., Frese, C.: Machine Vision: Automated Visual Inspection—Theory, Practice and Applications. Springer, Berlin (2016)

[3]

Blahusch, G., Eckstein, W., Steger, C., Lanser, S.: Algorithms and evaluation of a high precision tool measurement system. In: 5th International Conference on Quality Control by Artificial Vision, pp. 31–36 (1999)

[4]

Brown, D.C.: Decentering distortion of lenses. Photogramm. Eng. 32(3), 444–462 (1966)

[5]

Brown, D.C.: Close-range camera calibration. Photogramm. Eng. 37(8), 855–866 (1971)

[6]

Eggers, M., Dikov, V., Mayer, C., Steger, C., Radig, B.: Setup and calibration of a distributed camera system for surveillance of laboratory space. Pattern Recognit. Image Anal. 23(4), 481–487 (2013)

Digital Library

[7]

Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 476–480 (1999)

Digital Library

[8]

Fitzgibbon, A.W.: Simultaneous linear estimation of multiple view geometry and lens distortion. In: Conference on Computer Vision and Pattern Recognition, vol. I, pp. 125–132 (2001)

[9]

Förstner, W., Wrobel, B.P.: Photogrammetric Computer Vision: Statistics, Geometry, Orientation and Reconstruction. Springer, Cham (2016)

[10]

Gross, H.: Handbook of Optical Systems, Volume 1: Fundamentals of Technical Optics. Wiley-VCH, Weinheim (2005)

[11]

Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)

Digital Library

[12]

Lanser, S.: Modellbasierte Lokalisation gestützt auf monokulare Videobilder. PhD thesis, Forschungs- und Lehreinheit Informatik IX, Technische Universität München (1997)

[13]

Lanser, S., Zierl, C., Beutlhauser, R.: Multibildkalibrierung einer CCD-Kamera. In: Sagerer, G., Posch, S., Kummert, F. (eds.) Mustererkennung, Informatik aktuell, pp. 481–491. Springer, Berlin (1995)

[14]

Lenhardt, K.: Optical systems in machine vision. In: Hornberg, A. (ed.) Handbook of Machine and Computer Vision, 2nd edn, pp. 179–290. Wiley-VCH, Weinheim (2017)

[15]

Lenz, R.: Lens distortion corrected CCD-camera calibration with co-planar calibration points for real-time 3D measurements. In: ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, pp. 60–67 (1987)

[16]

Lenz, R., Fritsch, D.: Accuracy of videometry with CCD sensors. ISPRS J. Photogramm. Remote Sens. 45(2), 90–110 (1990)

[17]

Luster, S.D., Batchelor, B.G.: Telecentric, Fresnel and micro lenses. In: Batchelor, B.G. (ed.) Machine Vision Handbook, pp. 259–281. Springer, London (2012)

[18]

Mosteller, F., Tukey, J.W.: Data Analysis and Regression. Addison-Wesley, Reading (1977)

[19]

MVTec Software GmbH: HALCON/HDevelop Operator Reference, Version 18.05 (2018)

[20]

Oram, D.: Rectification for any epipolar geometry. In: British Machine Vision Conference, pp. 653–662 (2001)

[21]

Philip, J.: An algorithm for determining the position of a circle in 3D from its perspective 2D projection. Technical Report TRITA-MAT-1997-MA-14, Department of Mathematics, KTH (Royal Institute of Technology), Stockholm (1997)

[22]

Pollefeys, M., Koch, R., Gool, L.V.: A simple and efficient rectification method for general motion. In: 7th International Conference on Computer Vision, vol. 1, pp. 496–501 (1999)

[23]

Roy, S., Meunier, J., Cox, I.J.: Cylindrical rectification to minimize epipolar distortion. In: Conference on Computer Vision and Pattern Recognition, pp. 393–399 (1997)

[24]

Steger, C.: Unbiased extraction of curvilinear structures from 2D and 3D images. Dissertation, Fakultät für Informatik, Technische Universität München (1998)

[25]

Steger, C.: Subpixel-precise extraction of lines and edges. In: International Archives of Photogrammetry and Remote Sensing, vol. XXXIII, part B3, pp. 141–156 (2000)

[26]

Steger, C.: A comprehensive and versatile camera model for cameras with tilt lenses. Int. J. Comput. Vis. 123(2), 121–159 (2017)

Digital Library

[27]

Steger, C., Ulrich, M., Wiedemann, C.: Machine Vision Algorithms and Applications, 2nd edn. Wiley-VCH, Weinheim (2018)

[28]

Sturm, P., Ramalingam, S., Tardif, J.P., Gasparini, S., Barreto, J.: Camera models and fundamental concepts used in geometric computer vision. Found. Trends Comput. Graph. Vis. 6(1–2), 1–183 (2010)

Digital Library

Cited By

Steger CUlrich M(2022)A Multi-view Camera Model for Line-Scan Cameras with Telecentric LensesJournal of Mathematical Imaging and Vision10.1007/s10851-021-01055-x64:2(105-130)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10851-021-01055-x
Steger CUlrich M(2020)A Camera Model for Line-Scan Cameras with Telecentric LensesInternational Journal of Computer Vision10.1007/s11263-020-01358-3129:1(80-99)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.1007/s11263-020-01358-3

Index Terms

A camera model for cameras with hypercentric lenses and some example applications
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Machine Vision and Applications

Machine Vision and Applications Volume 30, Issue 6

Sep 2019

96 pages

ISSN:0932-8092

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Copyright © 2019 Springer-Verlag GmbH Germany, part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2019

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Cited By

Steger CUlrich M(2022)A Multi-view Camera Model for Line-Scan Cameras with Telecentric LensesJournal of Mathematical Imaging and Vision10.1007/s10851-021-01055-x64:2(105-130)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10851-021-01055-x
Steger CUlrich M(2020)A Camera Model for Line-Scan Cameras with Telecentric LensesInternational Journal of Computer Vision10.1007/s11263-020-01358-3129:1(80-99)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.1007/s11263-020-01358-3

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