Abstract
Object recognition and localization methods in RoboCup work on color segmented camera images. Unfortunately, color labeling can be applied to object recognition tasks only in very restricted environments, where different kinds of objects have different colors. To overcome these limitations we propose an algorithm named the Contracting Curve Density (CCD) algorithm for fitting parametric curves to image data. The method neither assumes object specific color distributions, nor specific edge profiles, nor does it need threshold parameters. Hence, no training phase is needed. In order to separate adjacent regions we use local criteria which are based on local image statistics. We apply the method to the problem of localizing the ball and show that the CCD algorithm reliably localizes the ball even in the presence of heavily changing illumination, strong clutter, specularity, partial occlusion, and texture.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Amini, A., Weymouth, T., Jain, R.: Using dynamic programming for solving variational problems in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(9), 855–867 (1990)
Baker, S., Nayar, S., Murase, H.: Parametric feature detection. International Journal of Computer Vision 27(1), 27–50 (1998)
Bandlow, T., Klupsch, M., Hanek, R., Schmitt, T.: Fast image segmentation, object recognition and localization in a robocup scenario. In: Third International Workshop on RoboCup (Robot World Cup Soccer Games and Conferences). LNCS, Springer, Heidelberg (1999)
Belongie, S., Carson, C., Greenspan, H., Malik, J.: Color- and texture based image segmentation using the expectation-maximization algorithm and its application to content-based image retrieval. In: Proc. International Conference on Computer Vision, pp. 675–682 (1998)
Blake, A., Isard, M.: Active Contours. Springer, Berlin (1998)
Bruce, J., Balch, T., Veloso, M.: Fast and inexpensive color image segmentation for interactive robots. In: International Conference on Intelligent Robots and Systems, IROS (2000)
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Chakraborty, A., Duncan, J.: Game-theoretic integration for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(1), 12–30 (1999)
Chesnaud, C., Refregier, P., Boulet, V.: Statistical region snake-based segmentation adapted to different physical noise models. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(11), 1145–1157 (1999)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Statist. Soc. B 39, 1–38 (1977)
Hanek, R.: The Contracting Curve Density Algorithm and its Application to Model-based Image Segmentation. In: Proc. Conf. Computer Vision and Pattern Recognition, pp. I.797–I.804 (2001)
Hanek, R., Schmitt, T.: Vision-Based Localization and Data Fusion in a System of Cooperating Mobile Robots. In: Proc. of the IEEE Intl. Conf. on Intelligent Robots and Systems, pp. 1199–1204. IEEE/RSJ (2000)
Hanek, R., Schmitt, T., Buck, S., and Beetz, M. Fast Image-based Object Localization in Natural Scenes. In Proc. of the IEEE Intl. Conf. on Intelligent Robots and Systems (submitted) (2002), IEEE/RSJ.
Hundelshausen, F., Behnke, S., Rojas, R.: An omnidirectional vision system that finds and tracks color edges and blobs. In: 5th International Workshop on RoboCup (Robot World Cup Soccer Games and Conferences). LNCS, Springer (2001)
Jamzad, M., Sadjad, B., Mirrokni, V., Kazemi, M., Chitsaz, H., Heydarnoori, A., Hajiaghai, M., Chiniforooshan, E.: A fast vision system for middle size robots in RoboCup. In: 5th International Workshop on RoboCup (Robot World Cup Soccer Games and Conferences). LNCS, Springer (2001)
Jones, T., Metaxas, D.: Image segmentation based on the integration of pixel affinity and deformable models. In: Proc. Conf. Computer Vision and Pattern Recognition, pp. 330–337 (1998)
Jonker, P., Caarls, J., Bokhove, W.: Fast and Accurate Robot Vision for Vision based Motion. In: Stone, P., Balch, T., Kraetzschmar, G. (eds.) 4th International Workshop on RoboCup (Robot World Cup Soccer Games and Conferences). LNCS, pp. 72–82. Springer, Heidelberg (2000)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)
Lenz, R., Tsai, R.Y.: Techniques for Calibration of the Scale Factor and Image Center for High Accuracy 3-D Machine Vision Metrology. IEEE Trans. on Pattern Analysis and Machine Intelligence 10(5), 713–720 (1988)
Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision 30(2), 79–116 (1998)
Luo, H., Lu, Q., Acharya, R., Gaborski, R.: Robust snake model. In: Proc. Conf. Computer Vision and Pattern Recognition, vol. I, pp. 452–457 (2000)
Malik, J., Belongie, S., Shi, J., Leung, T.: Textons, contours and regions: Cue integration in image segmentation. In: Proc. International Conference on Computer Vision, pp. 918–925 (1999)
Manduchi, R.: Bayesian fusion of color and texture segmentations. In: Proc. International Conference on Computer Vision, pp. 956–962 (1999)
Marques, C., Lima, P.: Vision-Based Self-Localization for Soccer Robots. In: International Conference on Intelligent Robots and Systems, IROS (2000)
Nakamura, T., Ebina, A., Imai, M., Ogasawara, T., Ishiguro, H.: Realtime Estimating Spatial Configurations between Multiple Robots by Trinagle and Enumeration Constraints. In: International Conference on Intelligent Robots and Systems, IROS (2000)
Nalwa, V., Binford, T.: On detecting edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 699–714 (1986)
Paragios, N., Deriche, R.: Coupled geodesic active regions for image segmentation: A level set approach. In: Proc. European Conference on Computer Vision, pp. 224–240 (2000)
Semple, J., Kneebone, G.: Algebraic projective geometry. Oxford University Press, Oxford (1952)
Simon, M., Behnke, S., Rojas, R.: Robust real time color tracking. In: Stone, P., Balch, T., Kraetzschmar, G. (eds.) 4th International Workshop on RoboCup (Robot World Cup Soccer Games and Conferences). LNCS, pp. 239–248. Springer, Heidelberg (2000)
Thirion, B., Bascle, B., Ramesh, V., Navab, N.: Fusion of color, shading and boundary information for factory pipe segmentation. In: Proc. Conf. Computer Vision and Pattern Recognition, pp. II.349-II.356 (2000)
Thrun, S., Fox, D., Burgard, W.: Monte carlo localization with mixture proposal distribution. In: Proc. of the AAAI National Conference on Artificial Intelligence, pp. 859–865 (2000)
Weigel, T., Kleiner, A., Diesch, F., Dietl, M., Gutmann, J.-S., Nebel, B., Stiegeler, P., Szerbakowski, B.: Cs freiburg 2001. In: 5th International Workshop on RoboCup (Robot World Cup Soccer Games and Conferences). LNCS. Springer, Heidelberg (2001)
Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)
Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(9), 884–900 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hanek, R., Schmitt, T., Buck, S., Beetz, M. (2003). Towards RoboCup without Color Labeling. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds) RoboCup 2002: Robot Soccer World Cup VI. RoboCup 2002. Lecture Notes in Computer Science(), vol 2752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45135-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-45135-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40666-2
Online ISBN: 978-3-540-45135-8
eBook Packages: Springer Book Archive