|Talare||Prof. Zoltan Kato (CBA)|
|Kommentar||University of Szeged, Hungary (Host: Natasa)|
|Titel||Region-based 2D and 3D image registration with medical applications|
|Sammanfattning||In this talk, I will present a novel framework developed in our lab for aligning a known shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the shapes and then compute the transformation parameters from these landmarks. The proposed framework avoids the correspondence problem and works directly with segmented regions, where the exact transformation is obtained as the solution of a polynomial system of equations. The method has been successfully applied to 2D and 3D multimodal medical image registration, industrial inspection, etc. Its robustness has also been demonstrated. The advantage of the proposed solution is that it is fast, easy to implement, has linear time complexity, works without established correspondences and provides an exact solution regardless of the magnitude of transformation.
Zoltan Kato received the M.S. degree in Computer Science from the University of Szeged, Hungary in 1990, the Ph.D. degree from the University of Nice doing his research at INRIA Sophia Antipolis, France in 1994, and the Doctor of Science degree from the Hunagrian Academy of Sciences in 2014. He has been a visiting research associate at the Computer Science Department of the Hong Kong University of Science & Technology, Hong Kong; an ERCIM postdoc fellow at CWI, Amsterdam, The Netherlands; and a visiting fellow at the School of Computing, National University of Singapore, Singapore. In 2002, he joined the Institute of Informatics, University of Szeged, Hungary where he is full professor. In 2012, he created the Research Group on Visual Computation (http://www.inf.u-szeged.hu/rgvc/). He has served on several program committees of major conferences (e.g. ICCV, CVPR, ECCV, ICIP, ICPR) and has been an Associate Editor for IEEE Transactions on Image Processing. Currently he is the President of the Hungarian Association for Image Processing and Pattern Recognition (KEPAF), representative of Hungary in the IAPR Governing Board and a Senior Member of IEEE. His research interests include registration, segmentation, remote sensing, camera calibration, 3D reconstruction, statistical image models, MCMC methods, and shape modeling.