000195774 001__ 195774
000195774 005__ 20190316235826.0
000195774 022__ $$a0162-8828
000195774 02470 $$2ISI$$a000349626200011
000195774 0247_ $$2doi$$a10.1109/TPAMI.2014.2343235
000195774 037__ $$aARTICLE
000195774 245__ $$aNon-Rigid Graph Registration using Active Testing Search
000195774 269__ $$a2015
000195774 260__ $$bInstitute of Electrical and Electronics Engineers$$c2015
000195774 336__ $$aJournal Articles
000195774 520__ $$aWe present a new approach for matching sets of branching curvilinear structures that form graphs embedded in R2 or R3 and may be subject to deformations. Unlike earlier methods, ours does not rely on local appearance similarity nor does require a good initial alignment. Furthermore, it can cope with non-linear deformations, topological differences, and partial graphs. To handle arbitrary non-linear deformations, we use Gaussian Processes to represent the geometrical mapping relating the two graphs. In the absence of appearance information, we iteratively establish correspondences between points, update the mapping accordingly, and use it to estimate where to find the most likely correspondences that will be used in the next step. To make the computation tractable for large graphs, the set of new potential matches considered at each iteration is not selected at random as in many RANSAC-based algorithms. Instead, we introduce a so-called Active Testing Search strategy that performs a priority search to favor the most likely matches and speed-up the process. We demonstrate the effectiveness of our approach first on synthetic cases and then on angiography data, retinal fundus images, and microscopy image stacks acquired at very different resolutions.
000195774 6531_ $$aDeformable Graph Matching
000195774 6531_ $$aRegistration
000195774 700__ $$aSerradell, Eduard
000195774 700__ $$aPinheiro, Miguel
000195774 700__ $$0245861$$aSznitman, Raphael$$g177109
000195774 700__ $$0242719$$aKybic, Jan$$g114880
000195774 700__ $$aMoreno-Noguer, Francesc
000195774 700__ $$0240252$$aFua, Pascal$$g112366
000195774 773__ $$j37$$k3$$q625-638$$tIEEE Transactions on Pattern Analysis and Machine Intelligence
000195774 8564_ $$s2137860$$uhttps://infoscience.epfl.ch/record/195774/files/subm.pdf$$yn/a$$zn/a
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000195774 937__ $$aEPFL-ARTICLE-195774
000195774 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000195774 980__ $$aARTICLE