Detección Eficiente de Elipses en Imágenes. Aplicación al Posicionamiento 3D de un Robot Industrial

  1. Eusebio de la Fuente López 1
  2. Félix Miguel Trespaderne 2
  1. 1 Instituto de Tecnologías Avanzadas de la Producción
  2. 2 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Revue:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Année de publication: 2012

Volumen: 9

Número: 4

Pages: 419-428

Type: Article

DOI: 10.1016/J.RIAI.2012.09.005 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Revista iberoamericana de automática e informática industrial ( RIAI )

Objectifs de Développement Durable

Résumé

En este artículo se presenta un algoritmo para la detección de elipses en imágenes, cuyo objetivo es el cálculo d e la posición 3D de una característica circular en una aplicación robótica. El algoritmo emplea un procedimiento estocástico RANSAC cuya eficiencia ha sido mejorada. El muestreo aleatorio ha sido sustituido por un muestreo guiado sobre las cadenas de contorno de la imagen, que son ordenadas de acuerdo a un criterio de probabilidad de formar parte de la elipse buscada. Esta estrategia disminuye notablemente la cantidad de muestras necesarias, permitiendo que el algoritmo sea adecuado para tiempo real.

Références bibliographiques

  • Ballard D.H., 1981. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition. Vol.13, No. 2, p. 111-122.
  • Bouguet, J.Y., 2010. Camera calibration toolbox for matlab.
  • Chaumette F., Hutchinson S., 2006. Visual servo control, part I: Basic approaches. IEEE Robotics and Automation Magazine, 13, 4, 82-90.
  • Cheng, Z., Liu, Y., 2004. Efficient technique for ellipse detection using restricted randomized Hough transform. In: Proceedings of International Conference on Information Technology, 2, 714–718.
  • Chum, O., Matas,J., 2005. Matching with PROSAC - progressive sampling concensus. In Proc. of the CVPR 2005.
  • DeSouza G.N., Kak A.C., 2002. Vision for mobile robot navigation: A survey. IEEE Pattern Analysis and Machine Intelligence, 24, 2, 237-267.
  • Fischler, M.A., Bolles, R.C., 1981. Random sample consensus: A paradigm for model fiting with applications to image analysis and automated cartography. Comm. ACM, 24, 6, 381– 395.
  • Fitzgibbon A., Pilu M., Fisher R.B., 1999. Direct least square fitting of ellipses, IEEE Pattern Analysis and Machine Intelligence, 21, 5, 476–480.
  • Fuente, E. de la, 2011. Robot guiado por visión.
  • Gracia L., Pérez C., 2010. Revisión de esquemas de control visual y propuesta de mejora. Revista Iberoamericana de Automática e Informática Industrial, 7, 2, 57-67.
  • Halir R., Flusser J., 1998. Numerically stable direct least squares fitting of ellipses. 6th Int. Conf. in Central Europe on Computer Graphics and Visualization, 125–132.
  • Hahn, K., Jung, S., Han, Y., Hahn, H., 2008. A new algorithm for ellipse detection by curve segments. Pattern Recognition Letters 29, 13, 1836- 1841.
  • Hartley R., Zisserman R., 2004. Multiple View Geometry in Computer Vision. Cambridge University Press.
  • Hermann, S., Klette, R., 2006. A comparative study on 2d curvature estimators. Research report CITR-TR-183, CITR, The University of Auckland, New Zealand.
  • Hough, P.V.C., 1962. Method and means for recognizing complex patterns. U.S. Patent 3069654.
  • Illingworth, J., Kittler, J., 1998. A survey of the Hough transform. Computer Vision, Graphics and Image Processing, 44, 87–116.
  • Kanatani, K., Liu, W., 1993. 3D interpretation of conics and orthogonality, CVGIP: Image Understanding. 58, 3, 286–301.
  • Kanatani, K., Ohta, N., 2004. Automatic detection of circular objects by ellipse growing. International Journal of Image and Graphics Vol. 4, No. 1 pp. 35–50.
  • Liu, Z., Qiao, H., 2009. Multiple ellipses detection in noisy environments: A hierarchical approach. Pattern Recognition, 42, 11, 2421-2433.
  • Mai, F., Hung Y.S., Zhong, H., Sze, W.F., 2008. A hierarchical approach for fast and robust ellipse extraction. Pattern Recognition, 41, 8, 2512–2524.
  • Marji, M., 2003. On the detection of dominant points on digital planar curves. PhD thesis, Wayne State University, Detroit, Michigan.
  • McLaughlin, R.A., 1998. Randomized Hough transform: improved ellipse detection with comparison. Pattern Recognition Letters 19, 299–305.
  • McLaughlin, R.A., Alder, M.D., 1998. The Hough transform versus the UpWrite. IEEE Pattern Analysis and Machine Intelligence, 396–400.
  • Miguel Trespaderne, F., Fuente, E. de la, 2009. Visually guided robot for radiator sealing. IEEE International Conference on Emergent Technologies and Factory Automation.
  • Myatt, D.R., Torr, P.H.S., Nasuto, S.J., Bishop, J.M., Craddock. R., 2002. Napsac: High noise, high dimensional robust estimation - it’s in the bag. BMVC02, 2, 458–467.
  • Mundy, J.L., Zisserman, A., 1992. Eds., Geometric Invariance in Computer Vision. MIT Press, Cambridge, Massachusetts, USA.
  • Rosenhahn B., Perwass C., Sommer G., 2004. CVonline: Foundations about 2D-3D pose estimation.
  • Rosin P.L., West G.A.W., 1995. Nonparametric segmentation of curves intovarious representations. IEEE Pattern Analysis and Machine Intelligence, 17, 12, 1140–1153.
  • Rousseeuw, P.J., Leroy, A.M., 1987. Robust Regression and Outlier Detection. Wiley.
  • Soria C., Roberti F., Carelli R., Sebastián J.M., 2008. Control Servo-Visual de un Robot Manipulador Planar Basado en Pasividad. Revista Iberoamericana de Automática e Informática Industrial, 5, 4, 54-61.
  • Thanh N. M., Ahuja S., Wu Q. M. J., 2009. A real-time ellipse detection based on edge grouping. IEEE International Conference on Systems, Man, and Cybernetics, 3280-3286.
  • Tordoff, B., Murray, D.W., 2002. Guided sampling and consensus for motion estimation. In Proc 7th European Conf on Computer Vision, Copenhagen, 82–98.
  • Xu, L., Oja E., Kultanena, P., 1990. A new curve detection method: randomized Hough transform (RHT). Pattern Recognition Letters, 11, 5, 331–338.
  • Zhang Z., 1997. Parameter estimation techniques: a tutorial with application to conic fitting. Image and Vision Computing 15, 59–76.
  • Zhang Z., 1998. A Flexible New Technique for Camera Calibration. Microsoft Research Technical Report MSR-TR-98-71, Microsoft Corporation.
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