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Colloquium: Robust Collaborative Trackers: Its Application for Medical and Natural Object Tracking
February 1, 2012 @ 4:00 pm - 5:00 pm
Robust Collaborative Trackers: Its Application for Medical and Natural Object Tracking
Robust object tracking is an interesting topic in computer vision and medical image analysis. The recent literature demonstrates that appropriate combinations of trackers can help balance stability and flexibility requirements. In this talk I will present a series of our recent work on robust collaborative tracking. I will start from an offline collaborative tracking algorithm and its application to track 3D heart chambers. It is a challenging task because of the relatively low image contrast and large size of the 3D volumetric data. The algorithm is well tested on thousands of clinical radiology data including ultrasound and CT. In order to adapt to changing environment in natural object tacking, the collaborative trackers are extended to an online tracking algorithm using two stage sparse optimization, all the trained classifiers and template library are online updated. The most recent online tracking algorithm using a novel discriminative dictionary learning method, called K-selection, and local sparse appearance model will be introduced at the end of the talk.
Brief Bio: Lin Yang is an assistant professor with the Division of Biomedical Informatics, Dept. of Biostatistics in the University of Kentucky. He received his B. E. and M. S. from Xian Jiaotong University in 1999 and 2002, and his Ph. D. in Dept. of Electrical and Computer Engineering from Rutgers, the State University of New Jersey in 2009. From 2009 – 2011 he was an assistant professor in the Department of Radiology in University of Medicine and Dentistry of New Jersey, and Department of Biomedical Engineering in Rutgers University. He did part of his research in Siemens Corporate Research and IBM T. J. Watson Research Center in 2007 and 2008. His major research interests are focus on medical image analysis, imaging informatics, computer vision and machine learning. He is also working on high performance computing and computer aided diagnostics. Lin Yang can be reached at lin.yang at uky dot edu.