Abstract
Applying real-time segmentation is a major issue when processing every frame of image sequences. In this paper, we propose a modification of the well known graph-cut algorithm to improve speed for discrete segmentation. Our algorithm yields real-time segmentation, using graph-cut, by performing a single cut on an image with regions of different resolutions, combining space-time pyramids and narrow bands. This is especially suitable for image sequences, as segment borders in one image are refined in the next image. The fast computation time allows one to use information contained in every image frame of an input image stream at 20 Hz, on a standard PC. The algorithm is applied to traffic scenes, using a monocular camera installed in a moving vehicle. Our results show the segmentation of moving objects with similar results to standard graph-cut, but with improved speed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. In: Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 359–374 (2001)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. In: IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 1222–1239 (2001)
Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72(2), 195–215 (2007)
Cremers, D., Soatto, S.: Motion competition: A variational framework for piecewise parametric motion segmentation. International Journal of Computer Vision 62(3), 249–265 (2005)
Greig, D.M., Porteous, B.T., Seheult, A.H.: Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society 51, 271–279 (1989)
Klappstein, J., Stein, F., Franke, U.: Monocular motion detection using spatial constraints in a unified manner. In: IEEE Intelligent Vehicles Symposium, pp. 261–267 (2006)
Kohli, P., Torr, P.H.S.: Effciently solving dynamic markov random fields using graph cuts. In: ICCV, pp. 922–929 (2005)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? In: IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 147–159 (2004)
Lombaert, H., Sun, Y., Grady, L., Xu, C.: A multilevel banded graph cuts method for fast image segmentation. In: ICCV, pp. 259–265 (2005)
Malcolm, J., Rathi, Y., Tannenbaum, A.: Multi-object tracking through clutter using graph cuts. In: ICCV, pp. 1–5 (2007)
Mooser, J., You, S., Neumann, U.: Real-time object tracking for augmented reality combining graph cuts and optical flow. In: ISMAR: Int. Symposium on Mixed and Augmented Reality, pp. 145–152 (2007)
Osher, S., Paragios, N.: Geometric Level Set Methods in Imaging,Vision,and Graphics. Springer, New York (2003)
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. In: Annual Review of Biomedical Engineering, vol. 2, pp. 315–337 (2000)
Rother, C., Kolmogorov, V., Blake, A.: “GrabCut”: interactive foreground extraction using iterated graph cuts. 23(3), 309–314 (August 2004)
Shapiro, L.G., Stockman, G.C.: Computer Vision, New Jersey, pp. 279–325. Prentice-Hall, Englewood Cliffs (2001)
Sinop, A.K., Grady, L.: Accurate banded graph cut segmentation of thin structures using laplacian pyramids. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 896–903. Springer, Heidelberg (2006)
Stein, F.: Efficient computation of optical flow using the census transform. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 79–86. Springer, Heidelberg (2004)
Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical report, Carnegie Mellon University Technical Report CMU-CS-91-132 (1991)
Wang, J., Bhat, P., Colburn, R.A., Agrawala, M., Cohen, M.F.: Interactive video cutout. SIGGRAPH 24, 585–594 (2005)
Wedel, A., Schoenemann, T., Brox, T., Cremers, D.: Warpcut - fast obstacle segmentation in monocular video. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 264–273. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vaudrey, T., Gruber, D., Wedel, A., Klappstein, J. (2008). Space-Time Multi-Resolution Banded Graph-Cut for Fast Segmentation. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-69321-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69320-8
Online ISBN: 978-3-540-69321-5
eBook Packages: Computer ScienceComputer Science (R0)