Nonstationary stochastic process-based robust estimation algorithm of near-infrared albedo

被引：0
|
作者：
Fang Z.-Q. [1 ]
Yu X.-S. [2 ]
Jia T. [1 ]
Wu C.-D. [2 ]
Li Y.-Q. [1 ]
Xu M. [1 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang
[2] Faculty of Robot Science and Engineering, Northeastern University, Shenyang
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 06期
关键词：
Albedo; Depth image; Infrared image; Near infrared; Robust estimation; Stochastic process;
D O I：
10.13195/j.kzyjc.2017.1617
中图分类号：
学科分类号：
摘要：
Albedo estimation plays an important role in many areas such as computer vision, computer graphics etc. A robust estimation algorithm of near-infrared albedo (RENA) based on nonstationary stochastic process is proposed in order to obtain albedo with high quality. This algorithm takes Kinect one as input and establishes an additive noise model of albedo. Simultaneously, the concept of robust shading estimation is proposed to simplify the nonstationary stochastic process model of albedo. Experiments show that estimation results of the proposed algorithm are better than other denoising algorithms, and it is suitable for high precision estimation of albedo images in indoor scenes. © 2019, Editorial Office of Control and Decision. All right reserved.
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页码：1151 / 1159
页数：8
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