Exploiting local structure for tracking plant cells in noisy images
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.