Mosinska A., Sznitman R., Glowacki P., Fua P. (2016), Active Learning for Delineation of Curvilinear Structures, in Conference on Computer Vision and Pattern Recognition
, Las Vegas, NEIEEE, Las Vegas, NE.
Glowacki P., Pinheiro M.A., Turetken E., Sznitman R., Lebrecht D., Holtmaat A., Kybic J., Fua P. (2014), Reconstructing Evolving Tree Structures in Time Lapse Sequences, in Conference on Computer Vision and Pattern Recognition
, Columbus, OHIEEE, Columbus, OH.
Turetken E., Benmansour F, Andres B., Pfister H., Fua P. (2013), Reconstructing Loopy Curvilinear Structures Using Integer Programming, in Conference on Computer Vision and Pattern Recognition
, Portland, ORIEEE, Portland, OR.
Turetken E., Benmansour F., Andres B., Glowacki P., Pfister H., Fua P., Reconstructing Curvilinear Networks Using Path Classifiers and Integer Programming, in IEEE Transactions on Pattern Analysis and Machine Intelligence
Tree-like structures appear at many different scales and in many different contexts. They can be micrometer scale dendrites in light microscopy image-stacks, centimeter-scale blood vessels in retinal scans, or meter-scale road networks in aerial images. Extracting them automatically and robustly is therefore of fundamental relevance to many scientific disciplines. However, even though the topic has received sustained attention ever since the inception of the field of Computer Vision, both robustness and automation remain elusive. Fully automated techniques exist but require very clean data; substantial amounts of manual intervention is required for any other kind.In this project, we will therefore develop a fully automated approach to addressing these shortcomings. We will first develop an approach to finding optimal trees that accounts both for global image and geometric properties. We will then implement a practical algorithm to build near-optimal ones in an acceptably short time, even though the underlying problem is closely related to one known to be NP-Hard.