Abstract
In this paper, we present a new method for detecting objects from multiple images. Unlike the general stereo-reconstruction methods, we propose a method for specific scenes only, which is motivated by the known fact that, in special circumstances, a specialised method will probably perform better than a general one. The theoretical model of the scene expects that the objects are created by the areas placed in a certain height over the scene base plane. The method is based on minimisation of an energy function. The function that has been found useful for the model is presented. For minimisation, the graph cut technique is used, which is modified by an iterative process in which the original edge weights are changed on the basis of the residual graph. The method has been tested on detecting the cars in parking lots; examples of the results are presented.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Deriche, R., Bouvin, C., Faugeras, O.: Front propagation and level-set approach for geodesic active stereovision. In: Third Asian Conference on Computer Vision, Hong Kong (1998)
Sun, J., Zheng, N.N., Shum, H.Y.: Stereo matching using belief propagation. IEEE Trans. Pattern Anal. Mach. Intell. 25, 787–800 (2003)
Ohta, Y., Kanade, T.: Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 139–154 (1985)
Roy, S., Cox, I.J.: A maximum-flow formulation of the n-camera stereo correspondence problem. In: ICCV, pp. 492–502 (1998)
Kolmogorov, V., Zabih, R.: Multi-camera Scene Reconstruction via Graph Cuts. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 82–96. Springer, Heidelberg (2002)
Hong, L., Chen, G.: Segment-based stereo matching using graph cuts. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 74–81 (2004)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vision 70, 109–131 (2006)
Kootstra, G., Bergström, N., Kragic, D.: Fast and automatic detection and segmentation of unknown objects. In: 2010 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 442–447 (2010)
Franke, M.: Color Image Segmentation Based on an Iterative Graph Cut Algorithm Using Time-of-Flight Cameras. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 462–467. Springer, Heidelberg (2011)
Ford, L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press (1962)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 359–374 (2001)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 65–81 (2004)
Huang, C., Wang, S.J.: A hierarchical bayesian generation framework for vacant parking space detection. IEEE Trans. Circuits Syst. Video Techn. 20, 1770–1785 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Holuša, M., Sojka, E. (2012). Object Detection from Multiple Images Based on the Graph Cuts. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-33179-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33178-7
Online ISBN: 978-3-642-33179-4
eBook Packages: Computer ScienceComputer Science (R0)