Keynote Speeches

<span title="">2019</span> <i title="IEEE"> 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) </i> &nbsp;
Currently, deep learning is the mainstream of machine learning and a most active area of artificial intelligence. Computer vision and image analysis are great application examples of deep learning. While computer vision and image analysis deal with existing images and produce related features (registration, segmentation, classification, etc.), tomography produces images of internal structures from externally measured features (line integrals, k-space samples, etc.) of underlying images.
more &raquo; ... , deep learning techniques are being actively developed worldwide for tomographic image reconstruction. We believe that "image reconstruction is a new frontier of machine learning" (IEEE Transactions on Medical Imaging 37 (2018) 1289), and promises major impacts on the development of solutions to many inverse problems. Over the past years, we have been working on data-driven bio-imaging, especially CT, MRI, and optical image reconstruction algorithms for superior imaging performance. In this presentation, we report our representative results, involving important applications and methodological innovations. We welcome collaborative opportunities. . He published the first spiral/helical cone-beam/multi-slice CT algorithm in 1991 and since then 100+ papers systematically contributed to theory, algorithms, artifact reduction and biomedical applications in this area. Currently, there are 100+ million medical CT scans yearly with a majority in the spiral/helical cone-beam/multi-slice mode. His group developed interior tomography theory and algorithms to solve the long-standing "interior problem" for high-fidelity local reconstruction, and enable omni-tomography ("all-in-one") with CT-MRI as an example. He initiated the area of bioluminescence tomography. He wrote 450+ journal publications, receiving a high number of citations and academic awards. His results were featured in Nature, Science, PNAS, and various news media. In 2016, he wrote the first perspective on neural-network-based tomographic imaging as the new frontier of machine learning. His team has been in collaboration with world-class groups and continuously well-funded by federal agencies and major imaging companies, actively translating machine learning techniques into imaging products. His interest includes x-ray CT, MRI, optical tomography, multimodality fusion, and machine learning. He is Lead Guest Editor of five IEEE Abstract: With the development of remote sensing technology, the Earth observation satellites have the characteristics of high resolution, wide coverage and multi-satellite network. So the acquired data grows geometrically, which brings severe challenges to the transmission, storage and processing of satellite data. On-board real-time processing technology is an effective means to solve these problems. Based on this technology, the data processing is completed within the satellite, such as interest region extraction, target detection and recognition, etc. Then the processing results can be directly transmitted to the users through broadcast downlink. The information acquisition delay can be reduced from hours to minutes. Because of the on-board processing technology, the application efficiency will be greatly improved, such as in the situations of emergency, disaster reduction and national security. This Keynote firstly expounds the development of Earth observation remote sensing satellites. And then, the significance and the technology development of the on-board real-time processing are introduced. Secondly, the technology difficulties and solving methods of on-board processing for optical or SAR payload are discussed. Finally, the development of the technology is prospected. Biography: Prof.
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