Performance of optical flow techniques
International Journal of Computer Vision
The computation of optical flow
ACM Computing Surveys (CSUR)
Improving accuracy of optical flow of heeger's original method on biomedical images
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Generation of synthetic image datasets for time-lapse fluorescence microscopy
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
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The availability of ground-truth flow field is crucial for quantitative evaluation of any optical flow computation method. The fidelity of test data is also important when artificially generated. Therefore, we generated an artificial flow field together with an artificial image sequence based on real-world sample image. The presented framework benefits of a two-layered approach in which user-selected foreground was locally moved and inserted into an artificially generated background. The background is visually similar to input sample image while the foreground is extracted from original and so is the same. The framework is capable of generating 2D and 3D image sequences of arbitrary length. Several examples of the version tuned to simulate real fluorescent microscope images are presented. We also provide a brief discussion.