IEEE Transactions on Pattern Analysis and Machine Intelligence
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
ECCV '90 Proceedings of the First European Conference on Computer Vision
Optical Snow and the Aperture Problem
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
A Super-Resolution Imaging Method Based on Dense Subpixel-Accurate Motion Fields
Journal of VLSI Signal Processing Systems
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Tomographic Reconstruction of Dynamic Cardiac Image Sequences
IEEE Transactions on Image Processing
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We propose a temporal modeling approach for determining image motion from a sequence of images wherein the inherent motion is periodic over time. To exploit the periodic nature of the motion, we use a Fourier harmonic representation to model the temporal evolution of the motion field for the entire sequence. We then determine the motion field simultaneously for the different image frames by estimating the parameters of this representation model, where the model order in the Fourier representation serves as a regularization parameter on the temporal coherence of the motion field. This approach can take advantage of the statistics of all the available data in the image sequence. In our experiments, we tested the proposed approach on several motion types at different noise levels, including translational motion, convergent/ divergent motion, and cardiac motion. Our results demonstrate that this approach could lead to more robust estimation of the motion field in the presence of strong imaging noise compared to a frame-by-frame estimation approach.