We recently introduced a continuous state space parametric model of spatio-temporal transformations and an algorithm, based on Kalman filtering, to represent motion in an image sequence describing a periodic phenomena. One advantage of this method is to simultaneously take into account all the sequence frames to robustly estimate the parameters of a unique spatial and periodic-temporal model. However, in 3D+time, a large number of parameters is required. In this paper, we propose a criterion based on motion energy to locally adapt the trajectory model and thus the temporal complexity of the model. The influence of the model order is illustrated on true 2D+time Magnetic Resonance Images (MRI) of the heart in order to motivate the proposed adaptative criteria. Quantitative results of the proposed adapted spatio-temporal motion model are given on synthetic 2D+time MRI sequences. Preliminary experiments show a significant impact notably regarding the parameter saving while preserving the accuracy of the motion estimates. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Delhay, B., Clarysse, P., & Magnin, I. E. (2007). Locally adapted spatio-temporal deformation model for dense motion estimation in periodic cardiac image sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4466 LNCS, pp. 393–402). Springer Verlag. https://doi.org/10.1007/978-3-540-72907-5_40
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