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20180711-20 郑银强:Data-driven Optimal Camera Response Selection and Design for ...

2018-7-5 17:40| 发布者: 程一-计算所| 查看: 241| 评论: 0

摘要: 报告嘉宾:郑银强(日本国立信息学研究所)报告时间:2018年07月11日(星期三)晚上20:00(北京时间)报告题目:Data-driven Optimal Camera Response Selection and Design for Spectral Reconstruction主持人:付 ...

报告嘉宾:郑银强日本国立信息学研究所

报告时间:2018年07月11日(星期三)晚上20:00(北京时间)

报告题目:Data-driven Optimal Camera Response Selection and Design for Spectral Reconstruction

主持人:付莹(北京理工大学)


报告人简介:

Yinqiang Zheng received his Bachelor degree from the Department of Automation, Tianjin University, Tianjin, China, in 2006, Master degree of engineering from Shanghai Jiao Tong University, Shanghai, China, in 2009, and Doctoral degree of engineering from the Department of Mechanical and Control Engineering, Tokyo Institute of Technology, Tokyo, Japan, in 2013. He is currently an assistant professor in the National Institute of Informatics, Japan. His research interests include 3D reconstruction, spectral imaging, biomedical imaging and mathematical optimization.


个人主页:


https://researchmap.jp/yinqiangzheng/


报告摘要:

Spectral construction from a multichannel image, in particular, from an RGB image, is promising in avoiding the cost and resolution issues of existing spectral devices. In this talk, I will first introduce our deep neural network based methods  for spectral reconstruction, and evaluate the effect of camera response onto spectral reconstruction accuracy. Later, I will show how to automatically select the optimal camera response from a candidate pool by introducing a camera response selection layer. Finally, the response selection layer will be replaced by a response design layer, which enables to design even better camera response for the purpose of spectral reconstruction. The algorithmically designed camera response has been realized  by using film filters and a prototype camera system constructed, which has drawn the curtain of data-driven and task-oriented optimal hardware design. 


参考文献:

[1] Shijie Nie, Ling Gu, Yinqiang Zheng, Antony Lam, Nobutaka Ono and Imari Sato, Deeply Learned Filter Response Functions for Hyperspectral Reconstruction, IEEE Conference on Computer Vision and Pattern Recognition (CVPR2018), pp. 4767-4776, Salk Lake City, Utah, USA, June, 2018.
[2] Boaz Arad, Ohad Ben-Shahar, Filter selection for hyper-spectral estimation. IEEE International Conference on Computer Vision (ICCV2017), pp.3172–3180, Venice, Italy, October, 2017.
[3] Yan Jia, Yinqiang Zheng, Lin Gu, Art Subpa-Asa, Antony Lam, Yoichi Sato and Imari Sato, From RGB to Spectrum for Natural Scenes via Manifold-based Mapping, IEEE International Conference on Computer Vision (ICCV2017), pp.4715-4723, Venice, Italy, October, 2017.


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特别鸣谢本次Webinar主要组织者:

VOOC责任委员:付莹北京理工大学

VODB协调理事:左旺孟(哈尔滨工业大学


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