? 可视媒体智能处理:图形学与视觉的融合
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (1) :110-121    DOI: 10.1007/s11390-017-1681-7
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可视媒体智能处理:图形学与视觉的融合
Ming-Ming Cheng1, Member, CCF, ACM, IEEE, Qi-Bin Hou1, Member, CCF, ACM, IEEE, Song-Hai Zhang2, Member, CCF, ACM, IEEE, and Paul L. Rosin1,3, Member, ACM, IEEE
1 College of Computer Science and Control Engineering, Nankai University, Tianjin 300071, China;
2 TNList, Tsinghua University, Beijing 100084, China;
3 School of Computer Science and Informatics, Cardiff University, Wales, CF10 3EU, U.K
Intelligent Visual Media Processing: When Graphics Meets Vision
Ming-Ming Cheng1, Member, CCF, ACM, IEEE, Qi-Bin Hou1, Member, CCF, ACM, IEEE, Song-Hai Zhang2, Member, CCF, ACM, IEEE, and Paul L. Rosin1,3, Member, ACM, IEEE
1 College of Computer Science and Control Engineering, Nankai University, Tianjin 300071, China;
2 TNList, Tsinghua University, Beijing 100084, China;
3 School of Computer Science and Informatics, Cardiff University, Wales, CF10 3EU, U.K

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摘要 近年来,计算机图形学和计算机视觉技术共同进步,涌现出了一批新的可视媒体分析和编辑的算法与应用。这种现象是由三个主要因素推动:i)互联网大数据带动了处理日益增长的大量资源的需求;ii)强大的处理工具,如深度神经网络,为学习如何处理异质视觉数据提供了有效的方法;iii)新的数据捕获设备,例如Kinect,架起了2D图像理解和3D模型分析算法之间的桥梁。这些近期才逐渐显现的推动因素,让我们相信计算机图形和计算机视觉研究群体的融合才刚刚开始。本文就计算机视觉技术和计算机图形技术如何相互推动进行综述,内容涵盖分析、编辑、合成和交互技术。我们还讨论现有技术中存在的问题,并对可能的进一步研究方向给出建议。
关键词计算机图形学   计算机视觉   综述   场景理解   图像编辑     
Abstract: The computer graphics and computer vision communities have been working closely together in recent years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon:1) the availability of big data from the Internet has created a demand for dealing with the ever-increasing, vast amount of resources; 2) powerful processing tools, such as deep neural networks, provide effective ways for learning how to deal with heterogeneous visual data; 3) new data capture devices, such as the Kinect, the bridge between algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques benefit computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions.
Keywordscomputer graphics   computer vision   survey   scene understanding   image manipulation     
Received 2016-08-11;
本文基金:

This research was sponsored by the National Natural Science Foundation of China under Grant Nos. 61572264 and 61373069, the National Key Research and Development Plan of China under Grant No. 2016YFB1001402, Huawei Innovation Research Program (HIRP), China Association for Science and Technology (CAST) Young Talents Plan, and Tianjin Short-Term Recruitment Program of Foreign Experts.

About author: Ming-Ming Cheng received his Ph.D. degree from Tsinghua University, Beijing, in 2012. Then he was a research fellow for two years with Prof. Philip Torr in Oxford. He is now an associate professor at Nankai University, Tianjin. His research interests include computer graphics, computer vision, and image processing. He has received the Google Ph.D. Fellowship Award, the IBM Ph.D. Fellowship Award, and the New Ph.D. Researcher Award from Chinese Ministry of Education.
引用本文:   
Ming-Ming Cheng, Qi-Bin Hou, Song-Hai Zhang, Paul L. Rosin.可视媒体智能处理:图形学与视觉的融合[J]  Journal of Computer Science and Technology , 2017,V32(1): 110-121
Ming-Ming Cheng, Qi-Bin Hou, Song-Hai Zhang, Paul L. Rosin.Intelligent Visual Media Processing: When Graphics Meets Vision[J]  Journal of Computer Science and Technology, 2017,V32(1): 110-121
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