Abstract
In this paper, a new algorithm for bimodal depth segmentation is presented. The method separates the background and the planar objects of arbitrary shapes lying in a certain height above the background using the information from the stereo image pair (more exactly, the background and the objects may lie on two distinct general planes). The problem is solved as a problem of minimising a functional. A new functional is proposed for this purpose that is based on evaluating the mismatches between the images, which contrasts with the usual approaches that evaluate the matches. We explain the motivation for such an approach. The minimisation is carried out by making use of the Euler-Lagrange equation and the level-set function. The experiments show the promising results on noisy synthetic images as well as on real-life images. An example of the practical application of the method is also presented.
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.
Similar content being viewed by others
References
Alvarez, L., Deriche, R., Weickert, J., Sanchez, J.: Dense disparity map estimation respecting image discontinuities: A PDE and scale-space based approach. Journal of Visual Communication and Image Representation, Special Issue on Partial Differential Equations in Image Processing, Computer Vision and Computer Graphics 13(1/2), 3–21 (2002)
Ben-Ari, R., Sochen, N.: Stereo matching with mumford-shah regularization and occlusion handling. IEEE Trans. Pattern Analysis and Machine Intelligence 32(11), 2071–2084 (2010)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 1222–1239 (2001)
Chan, T.F., Vese, L.A.: An active contour model without edges. In: International Conference Scale-Space Theories in Computer Vision, pp. 141–151 (1999)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)
Chan, T.F., Vese, L.A.: A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision 50, 271–293 (2002)
Cox, I.J., Hingorani, S.L., Rao, S.B., Maggs, B.M.: A maximum likelihood stereo algorithm. Computer Vision and Image Understanding 63, 542–567 (1996)
Deriche, R., Bouvin, C., Faugeras, O.: Level-set approach for stereo. Investigative Image Processing 2942(1), 150–161 (1997)
Deriche, R., Bouvin, C., Faugeras, O.: Front propagation and level-set approach for geodesic active stereovision. In: IEEE Workshop on Visual Surveillance, pp. 56–63 (1998)
Fabián, T.: An algorithm for parking lot occupation detection. In: Proceedings of the 7th Computer Information Systems and Industrial Management Applications, pp. 165–170 (2008)
Faugeras, O., Keriven, R.: Variational principles, surface evolution, pde’s, level set methods and the stereo problem. IEEE Trans. on Image Processing 7, 336–344 (1999)
Hsu, C.Y., Yang, C.H., Wang, H.C.: Topological control of level set method depending on topology constraints. Pattern Recognition Letters 66, 537–546 (2008)
Klaus, A., Sormann, M., Karner, K.F.: Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 3, pp. 15–18 (2006)
Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 508–515 (2001)
Leventon, M.E., Grimson, W.E.L., Faugeras, O., Wells, W.: Level set based segmentation with intensity and curvature priors. In: Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 4–11 (2000)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Analysis and Machine Intelligence 17, 158–175 (1995)
Nikolova, M., Esedoglu, S., Chan, T.F.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal of Applied Mathematics 66(5), 1632–1648 (2006)
Ohta, Y., Kanade, T.: Stereo by intra- and inter-scanline search using dynamic programming. IEEE Trans. Pattern Analysis and Machine Intelligence 7(1), 139–154 (1985)
Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: algorithms based on the Hamilton-Jacobi formulation. Journal of Computational Physics 79, 12–49 (1988)
Rousson, M., Paragios, N.: Prior Knowledge, Level Set Representations & Visual Grouping. International Journal of Computer Vision 76(3), 231–243 (2008)
Sojka, E., Gaura, J., Krumnikl, M.: Active Contours without Edges and with Simple Shape Priors. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 730–741. Springer, Heidelberg (2008), http://dx.doi.org/10.1007/978-3-540-88458-3_66
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
Krumnikl, M., Sojka, E., Gaura, J. (2012). A New Level-Set Based Algorithm for Bimodal Depth Segmentation. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-33140-4_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33139-8
Online ISBN: 978-3-642-33140-4
eBook Packages: Computer ScienceComputer Science (R0)