Joakim Jitén, Bernard Merialdo


Statistical modeling methods have become critical for many image processing problems, such as segmentation, compression and classification. In this paper we are proposing and experimenting a computationally efficient simplification of 3-Dimensional Hidden Markov Models. Our proposed model relaxes the dependencies between neighboring state nodes to a random uni-directional dependency by introducing a three dimensional dependency tree (3D-DT HMM). To demonstrate the potential of the model we apply it to the problem of tracking objects in a video sequence. We explore various issues about the effect of the random tree and smoothing techniques. Experiments demonstrate the potential of the model as a tool for tracking video objects with an efficient computational cost.


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Paper Citation

in Harvard Style

Jitén J. and Merialdo B. (2007). VIDEO MODELING USING 3-D HIDDEN MARKOV MODEL . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 191-198. DOI: 10.5220/0002044701910198

in Bibtex Style

author={Joakim Jitén and Bernard Merialdo},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},

in EndNote Style

JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
SN - 978-972-8865-73-3
AU - Jitén J.
AU - Merialdo B.
PY - 2007
SP - 191
EP - 198
DO - 10.5220/0002044701910198