Reconstructing Evolving Tree Structures in Time Lapse Sequences

We propose an approach to reconstructing tree structures that evolve over time in 2D images and 3D image stacks such as neuronal axons or plant branches. Instead of reconstructing structures in each image independently, we do so for all images simultaneously to take advantage of temporal-consistency constraints. We show that this problem can be formulated as a Quadratic Mixed Integer Program and solved efficiently. The outcome of our approach is a framework that provides substantial improvements in reconstructions over traditional single time-instance formulations. Furthermore, an added benefit of our approach is the ability to automatically detect places where significant changes have occurred over time, which is challenging when considering large amounts of data.


Présenté à:
Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 2014
Année
2014
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 Notice créée le 2014-04-16, modifiée le 2019-12-05

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