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Flexible FTIR spectral imaging enhancement for industrial robot infrared vision sensing

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成果类型:
期刊论文
作者:
Liu, Tingting;Liu, Hai*;Li, You-Fu;Chen, Zengzhao;Zhang, Zhaoli;...
通讯作者:
Liu, Hai
作者机构:
[Liu, Sannyuya; Liu, Tingting; Chen, Zengzhao; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
[Liu, Tingting] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA.
[Li, You-Fu; Liu, Hai] City Univ Hong Kong, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China.
[Li, You-Fu] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China.
通讯机构:
[Liu, Hai] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Infrared imaging spectrum;intelligent vision system;robot infrared vision sensing;visual tracking
期刊:
IEEE Transactions on Industrial Informatics
ISSN:
1551-3203
年:
2020
卷:
16
期:
1
页码:
544-554
基金类别:
Manuscript received March 28, 2019; revised July 1, 2019; accepted July 10, 2019. Date of publication August 12, 2019; date of current version January 4, 2020. This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1401300 and Grant 2017YFB1401303, in part by the National Natural Science Foundation of China under Grant 61875068, Grant 61873220, Grant 61673329, and Grant 61505064, in part by the Research Grants Council of Hong Kong under Project CityU 11205015 and Project CityU 11255716, and in part by the Fundamental Research Funds for the Central Universities under Grant CCNU18ZDPY10 and Grant 2017YBZZ009. Paper no. TII-19-1099. (Tingting Liu and Hai Liu contributed equally to this article.) (Corresponding author: Hai Liu.) T. Liu is with the National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China, and also with the School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail:,tingtin3@andrew.cmu.edu).
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Infrared (IR) spectral imaging sensing is a powerful visual technique for industrial material recognition in robot vision systems. However, the imaging sensing data have issues of random noise and band overlap. Resolution enhancement is usually the first step in the preprocessing procedure of industrial robot vision sensing. In this article, we develop a resolution-enhancement algorithm with total variation (TV) constraints for the degraded Fourier transform IR (FTIR) spectrum due to overlap and noise degradation in the robot vision sensing. The kernel function is calculated using the spectrom...

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