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SUMMARY:Ground Truth Correction for Topologically Better Delineation of Cu
 rvilinear Structures
DTSTART:20190618T090000
DTEND:20190618T110000
DTSTAMP:20260507T213542Z
UID:f0dcafb55cdccb5bd6b5f8c073a733de353b4e2b13def10c842467b8
CATEGORIES:Conferences - Seminars
DESCRIPTION:Doruk Oner\nEDIC candidacy exam\nExam president: Prof. Bertran
 d Merminod\nThesis advisor: Prof. Pascal Fua\nCo-examiner: Dr. Graham Knot
 t\n\nAbstract\nDelineation of curvilinear structures is a popular directio
 n of research in computer vision since such structures are used in many re
 al world problems like biomedical image analysis. The idea is to find cent
 erlines of the desired structures in a given image while preserving the ov
 erall topology of the structure. In this proposal\, we analyzed three work
 s that are related to this problem. We presented the problems they solved\
 , the methods they used\, results of the works and how we can integrate th
 ese work to our research. The first paper focuses on an end-to-end 3D conv
 olutional network to produce individual segments. The second paper is a su
 rvey paper on deformable models\, their extensions and applications. The t
 hird paper is on a new method on unsupervised reconstruction of 3D point c
 louds. Finally\, we talked about our current research\, and future plans a
 nd discussed how to use these papers to improve our research to obtain top
 ologically better capture of curvilinear structures.\n\nBackground papers\
 nFlood-fliing Networks by Michal Januszewski\, by Jeremy Maitin-Shepard\, 
 Peter Li\, Jörgen Kornfeld\, Winfried Denk and Viren Jain\,  CoRR 2016.\
 nDeformable models in medical image analysis: a survey by Tim McInerney an
 d Demetri Terzopoulus\, Medical Image Analysis (MIA) 1996.\nFoldingNet: Po
 int Cloud Auto-Encoder via Deep Grid Deformation\, by Yaoqing Yang\, Chen 
 Feng\, Yiru Shen and Dong Tian\, IEEE/CVF Conference on Computer Vision an
 d Pattern Recognition 2017.
LOCATION:BC 329 https://plan.epfl.ch/?room==BC%20329
STATUS:CONFIRMED
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