Computing occluding and transparent motions
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Simple points, topological numbers and geodesic neighborhoods in cubic grids
Pattern Recognition Letters
A fast level set method for propagating interfaces
Journal of Computational Physics
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and Characterization of Highly Deformable Cloud Structures
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
ACM Computing Surveys (CSUR)
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
A topology preserving level set method for geometric deformable models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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This paper introduces a novel framework for the automated tracking of cells, with a particular focus on the challenging situation of phase contrast microscopic videos. Our framework is based on a topology preserving variational segmentation approach applied to normal velocity components obtained from optical flow computations, which appears to yield robust tracking and automated extraction of cell trajectories. In order to obtain improved trackings of local shape features we discuss an additional correction step based on active contours and the image Laplacian which we optimize for an example class of transformed renal epithelial (MDCK-F) cells. We also test the framework for human melanoma cells and murine neutrophil granulocytes that were seeded on different types of extracellular matrices. The results are validated with manual tracking results.