Recursive map displacement field estimation and its applications. Brailean, J. & Katsaggelos, A. In Proceedings of 3rd IEEE International Conference on Image Processing, volume 1, pages 917–920, 1996. IEEE. ![Recursive map displacement field estimation and its applications [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this paper we briefly describe some of our work on the rise of stochastic models to describe the displacement vector field (DVF) in an image sequence. Specifically, autoregresive models are used which describe the abrupt transitions in the DVF with the use of a line process, but also result in spatio-temporally recursive structures. The use of such models in developing maximum a posteriori estimators for the DVF and the line process is subsequently described. Finally, the extension and application of the resulting estimator to the problems of object tracking, video compression and restoration of video sequences is briefly reviewed.
@inproceedings{James1996a,
abstract = {In this paper we briefly describe some of our work on the rise of stochastic models to describe the displacement vector field (DVF) in an image sequence. Specifically, autoregresive models are used which describe the abrupt transitions in the DVF with the use of a line process, but also result in spatio-temporally recursive structures. The use of such models in developing maximum a posteriori estimators for the DVF and the line process is subsequently described. Finally, the extension and application of the resulting estimator to the problems of object tracking, video compression and restoration of video sequences is briefly reviewed.},
author = {Brailean, J.C. and Katsaggelos, A.K.},
booktitle = {Proceedings of 3rd IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.1996.559649},
isbn = {0-7803-3259-8},
pages = {917--920},
publisher = {IEEE},
title = {{Recursive map displacement field estimation and its applications}},
url = {http://ieeexplore.ieee.org/document/559649/},
volume = {1},
year = {1996}
}
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