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Multi-modal Image Processing
时间:2018-08-13 23:00    点击:   所属单位:物理与光电工程学院
讲座名称 Multi-modal Image Processing
讲座时间 2018-08-16 10:45:00
讲座地点 北校区图书馆西裙楼三楼报告厅
讲座人 Prof. Mark R. Pickering
讲座人介绍

Mark Pickering received his Masters by Research and PhD from UNSW, Sydney, Australia. He is currently a Professor with the School of Engineering and Information Technology at UNSW Canberra. He has co-authored 2 books, 2 book chapters, 62 journal articles and over 100 international conference papers in the fields of video coding, medical imaging and remote sensing. He has been actively involved in the development of the MPEG international standards for audio-visual communications and served for three years as head of the Australian delegation to MPEG. He served as the general co-chair for the 2015 Picture Coding Symposium held in Cairns, Australia and is currently serving as the General Co-Chair of the 2018 Digital Image Computing: Techniques and Applications conference which will be held in Canberra, Australia.

讲座内容

Image registration is a fundamental task used to align two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. In the registration process, the image data acquired from one or more than one source is aligned and combined into a unique coordinate system. Image registration is widely used in different areas such as remote sensing, medical imaging and computer vision. Unlike mono-modal image registration, in which the images to be registered are acquired by the same sensor, the images in multi-modal image registration can be taken from different devices or imaging protocols. Hence, multi-modal registration is much more challenging than mono-modal registration.

In this talk I will present an overview of multi-modal image registration followed by our results from some recent projects involving multi-model image registration for data fusion. The first of these projects requires multi-modal registration to fuse 2-D video x-rays with a 3D CT scan of the bines in a knee joint. The resulting fused data provides a moving 3D model of the knee joint which can be viewed from any angle. The second of these projects requires multi-modal registration to fuse CT and MRI data of a human brain to provide an image with high contrast for both bone and soft tissue.

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