Abstract
This paper presents an approach for video object segmentation. The main idea of our approach is to generate a planar, triangulated, and labeled graph that describes the scene, foreground objects and background. With the help of the Kanade-Lucas-Tomasi Tracker, corner points are tracked within a video sequence. Then the movement of the points adaptively generates a planar triangulation. The triangles are labeled as rigid, articulated, and separating depending on the variation of the length of their edges.
Partially supported by the Austrian Science Fund under grants P18716-N13 and S9103-N13.
Chapter PDF
Similar content being viewed by others
References
Celasun, I., Tekalp, A.M., Gokcetekin, M.H., Harmanci, D.M.: 2-d mesh-based video object segmentation and tracking with occlusion resolution. Signal Processing: Image Communication 16(10), 949–962 (2001)
Alatan, A.A., Onural, L., Wollborn, M., Mech, R., Tuncel, E., Sikora, T.: Image sequence analysis for emerging interactive multimedia services. IEEE Transactions on Circuits and Systems for Video Technology 8(7), 802–813 (1998)
Altunbasak, Y., Eren, P.E., Tekalp, A.M.: Region-based parametric motion segmentation using color information. Graphical Models and Image Processing 60(1), 13–23 (1998)
Castagno, R., Ebrahimi, T., Kunt, M.: Video segmentation based on multiple features for interactive multimedia applications. IEEE Transactions on Circuits and Systems for Video Technology 8(5), 562–571 (1998)
Chen, H.T., Liu, T.L., Fuh, C.S.: Segmenting highly articulated video objects with weak-prior random forests. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 373–385. Springer, Heidelberg (2006)
Celasun, I., Tekalp, A.: Optimal 2-d hierarchical content-based mesh design and update for object-based video. IEEE Transactions on Circuits and Systems for Video Technology 10(7), 1135–1153 (2000)
Li, H., Lin, W., Tye, B., Ong, E., Ko, C.: Object segmentation with affine motion similarity measure. In: IEEE International Conference on Multimedia and Expo, 2001, August 22-25. ICME 2001, pp. 841–844 (2001)
Tekalp, A., Van Beek, P., Toklu, C., Gunsel, B.: Two-dimensional mesh-based visual-object representation for interactive synthetic/natural digital video. Proceedings of the IEEE 86(6), 1029–1051 (1998)
Artner, N., López Mármol, S.B., Beleznai, C., Kropatsch, W.G.: Kernel-based tracking using spatial structure. In: 32nd annual workshop of the Austrian Association for Pattern Recognition, Austria, pp. 103–114 (2008)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. Image and Vision Computing 22(10), 761–767 (2004)
Birchfeld, S.: Klt: An implementation of the kanade-lucas-tomasi feature tracker (March 2008), http://www.ces.clemson.edu/~stb/klt/
Moss, S., Wilson, R.C., Hancock, E.R.: A mixture model for pose clustering. Pattern Recognition Letters 20(11–13), 1093–1101 (1999)
Klette, R., Rosenfeld, A.: Digital Geometry. Morgan Kaufmann, San Francisco (2004)
Shi, J., Tomasi, C.: Good features to track. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 593–600. IEEE Computer Society Press, Los Alamitos (June 1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mármol, S.B.L., Artner, N.M., Ion, A., Kropatsch, W.G., Beleznai, C. (2008). Video Object Segmentation Using Graphs . In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_89
Download citation
DOI: https://doi.org/10.1007/978-3-540-85920-8_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85919-2
Online ISBN: 978-3-540-85920-8
eBook Packages: Computer ScienceComputer Science (R0)