第十届国际图象图形学学术会议 投稿倒计时7天 2019 征稿进行中 ICIG (第十届国际图像图形学学术会议)

雷锋网 AI 科技评论消息，第十届国际图象图形学学术会议（ICIG 2019）将于 2019 年 8 月 23-25 日在北京友谊宾馆召开，主题为「人工智能时代的图像图形前沿研究」。国际图象图形学学术会议（ICIG）是中国图象图形学学会主办的最高级别的系列国际会议，创建于 2000 年，每两年举办一届，迄今已经成功举办九届。

会议征文将于 5 月 20 日截止，现面向广大科技工作者公开征集学术论文（英文），会议论文集将由 Springer出版社出版，并被 EI 和 CPCI 检索。优秀的论文经扩展后将推荐到国内外高质量期刊的特刊（Pattern Recognition Letters、Cognitive Computation、Pattern Recognition 等）。

投稿系统网址：

今年大会共计 3 个特邀报告、5 个热点前沿论坛、1 个顶会顶刊论坛、1 个博士生论坛、2 个技术竞赛、多场创新成果展。大会将为图像图形领域的专家学者和产业界同仁提供展示创新成果、探讨未来发展的平台，大会详情如下。

大会主题

人工智能时代的图像图形前沿研究

征文范围

特邀报告

Title:The Shape of Deformations

Let us say that a frame is given by three ‘sticks’ (of equal lengths) meeting in one common point. We are interested in representing the orientation and the ‘shape’ of the frame. Orientation is the rotation relative to a reference frame; and ‘shape’ is the deformation relative to a reference frame. It turns out that any frame can be turned into any other frame by a Möbius transformation. This viewpoint reveals that rotations are points on a 3-sphere, the so-called unit quaternions. Unit quaternions are well-known and quite useful as a representation for rotations in space — they are continuous in the variables, minimal in the sense that at least four coordinates are necessary for a continuous representation, and they come with a natural metric that allows us to measure the ‘amount’ of rotation, i.e. the angle. The viewpoint of Möbius transformations also reveals, and this is the new aspect of this work, that deformations are points on a hyperboloid. So ‘shape’ can be described as a point in hyperbolic space. This is a representation that, just like unit quaternions, is continuous, small, and comes with a natural metric that allows measuring the amount of deformation.

Biography：

MarcAlexais a Professor in the Faculty of Electrical Engineering and Computer Science at the Technical University of Berlin and heads the ComputerGraphicsgroup. He is interested in creating, processing, and manufacturing shapes, as well as intuitive interFaces for these tasks. He holds a masters and PhD degree in Computer Science from Darmstadt University of Technology. He has been Technical Papers Chair for SIGGRAPH 2013, and has chaired several other committees and boards. He received an ERC Starting Grant, the Heinz Maier-Leibnitz prize, as well as other cross-disciplinary grants and awards. He is currently the editor in chief of ACM Transactions on Graphics.

Title:Improved Scene Understanding by Combining Sight and Sound

In traditional computer vision reseArch, we were careful to strip and ignore the soundtrack. In the same manner, audio analysis traditionally ignored the available visual information in a video.I will describe how better scene understanding can be gained by Using both sight and sound in video. One example will show how speech can be enhanced when using a video showing the face of the speaker. Also, video surveillance cases will be shown where the sound can enhance the understanding of scene activity.

Biography：

Shmuel Peleg received his Ph.D. in Computer Science from the University of Maryland in 1979, and since 1981 he is a professor at the Hebrew University of Jerusalem.Shmuel published over 150 technical papers in computer vision and image processing, and holds 23 US patents. He founded several startup companies, and his latest company, Briefcam, was acquired by Canon on 2018. Shmuel served as a committee member of numerous conferences, e.g. general co-chair of ICPR 1994, CVPR 2011, ICCP 2013, and CVPR 2018.

Title: Low quality fingerprint recognition

Fingerprint recognition is one of the most widely used biometric Technologies. Despite its wide applications, the recognition rate of low quality fingerprints, such as fingerprints of elderly people, latent fingerprints from crime scenes, and fingerprints of very dry or wet fingers, is still far from satisfactory. This talk will provide an introduction of our recent progress for low quality fingerprint recognition, including fingerprint enhancement, skin distortion rectification, and dense registration.

Biography：

Dr. Jie Zhou received the PhD degree from the Institute of Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology (HUST), Wuhan, China, in 1995. He joined in the Department of Automation, Tsinghua University in 1997. Since 2003, he has been a full professor in the Department of Automation, Tsinghua University. From 2015 to 2018, he was the Chair of Department of Automation, Tsinghua University. From 2018 to now, he is the Dean of Graduate School, Tsinghua University. His research interests include pattern recognition, computer vision, and image processing. In recent years, he has authored more than 300 papers in peer-reviewed journals and conferences. Among them, more than 50 papers have been published in top journals and conferences (IEEE T-PAMI, T-IP, CVPR and ICCV). He received the National Outstanding Youth Foundation of China Award in 2012. He is a fellow of IAPR and Associate Editor of IEEE T-PAMI.

会议邮箱

icig@csig.org.cn

会议网址

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