摘要
反射率估计在计算机视觉、计算机图形学等领域具有重要作用.为了精确获取反射率,提出一种基于非稳态随机过程的近红外反射率鲁棒估计算法(RENA).该算法以Kinect二代传感器采集结果计算初始反射率,并建立反射率加性噪声模型,同时提出光照度鲁棒估计的概念,简化反射率图像非稳态随机过程模型.实验表明, RENA算法的反射率估计结果优于其他去噪算法,适用于室内场景的反射率图像高精度估计.
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.
作者
房卓群
于晓升
贾同
吴成东
李永强
许茗
FANG Zhuo-qun;YU Xiao-sheng;JIA Tong;WU Cheng-dong;LI Yong-qiang;XU Ming(College of Information Science and Engineering,Northeastern University,Shenyang 110004,China;Faculty of Robot Science and Engineering,Northeastern University,Shenyang 110004,China)
出处
《控制与决策》
EI
CSCD
北大核心
2019年第6期1151-1159,共9页
Control and Decision
基金
国家自然科学基金项目(61701101
61603080
U1613214
U1713216)
国家机器人重点专项项目(2017YFB1300900)
中央高校基本科研业务费项目(N170402008
N172603001
N172604004)
沈阳市科研基金项目(17-87-0-00)
关键词
近红外光
反射率
鲁棒估计
随机过程
深度图像
红外图像
near infrared
albedo
robust estimation
stochastic process
depth image
infrared image
