Abstract
Lines are particularly important features for different tasks such as calibration, structure from motion, 3D reconstruction in computer vision. However, line detection in catadioptric images is not trivial because the projection of a 3D line is a conic eventually degenerated. If the sensor is calibrated, it has been already demonstrated that each conic can be described by two parameters. In this way, some methods based on the adaptation of conventional line detection methods have been proposed. However, most of these methods suffer from the same disadvantages than in the perspective case (computing time, accuracy, robustness, ...). In this paper, we then propose a new method for line detection in central catadioptric image comparable to the polygonal approximation approach. With this method, only two points of a chain allows to extract with a very high accuracy a catadioptric line. Moreover, this algorithm is particularly fast and is applicable in realtime. We also present experimental results with some quantitative and qualitative evaluations in order to show the quality of the results and the perspectives of this method.
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References
Benosman, R., Kang, S.B.: Panoramic Vision: Sensors, Theory, Applications. Springer, Heidelberg (2001)
Baker, S., Nayar, S.K.: A theory of single-viewpoint catadioptric image formation. International Journal on Computer Vision 35(2), 175–196 (1999)
Barreto, J.P., Araújo, H.: Geometric properties of central catadioptric line images. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 237–251. Springer, Heidelberg (2002)
Barreto, J.P.: General Central Projection Systems: Modeling, Calibration and Visual Servoing. PhD Thesis, University of Coimbra (2003)
Geyer, C., Daniilidis, K.: Catadioptric projective geometry. International Journal of Computer Vision 45(3), 223–243 (2001)
Zhang, Z.: Parameter estimation techniques: a tutorial with application to conic fitting. Image Vision Comput. 15(1), 59–76 (1997)
Vasseur, P., Mouaddib, E.M.: Central catadioptric line detection. In: BMVC04 (2004)
Ying, X., Hu, Z.: Catadioptric line features detection using hough transform. In: ICPR, vol. 4, pp. 839–842. IEEE Computer Society Press, Los Alamitos (2004)
Mei, C., Malis, E.: Fast central catadioptric line extraction, estimation, tracking and structure from motion. In: IROS (2006)
Barreto, J.P., Araújo, H.: Fitting conics to paracatadioptric projections of lines. Computer Vision and Image Understanding 101(3), 151–165 (2006)
Vandeportaele, B., Cattoen, M., Marthon, P.: A fast detector of line images acquired by an uncalibrated paracatadioptric camera. In: ICPR, vol. 3, pp. 1042–1045. IEEE Computer Society Press, Los Alamitos (2006)
Fitzgibbon, A.W., Fisher, R.B.: A buyer’s guide to conic fitting. In: BMVC95 (1995)
Jennings, A., McKeown, J.J.: Matrix Computation, 2nd edn. John Wiley & Sons, New York (1992)
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Bazin, J.C., Demonceaux, C., Vasseur, P. (2007). Fast Central Catadioptric Line Extraction. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_4
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DOI: https://doi.org/10.1007/978-3-540-72849-8_4
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