A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Tracking Cell Signals in Fluorescent Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
IEEE Transactions on Image Processing
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In this paper, we present a local graph matching based method for tracking cells and cell divisions in noisy images. We work with plant cells, where the cells are tightly clustered in space and computing correspondences across time can be very challenging. The local graph matching method is able to track the cells and cell divisions even when significant portions of the images are corrupted due to sensor noise in the imaging process. The geometric structure and topology of the cells' relative positions are efficiently exploited to solve the tracking problem using the local graph matching technique. Using this method we can track almost all of the properly segmented cells, even when some of those images are highly noisy.