Material Recognition Based on a Pulsed Time-of-Flight Camera

被引：0
|
作者：
Zhang, Jizhong [1 ]
Lang, Shinan [1 ]
Wu, Qiang [1 ]
Liu, Chuan [1 ]
机构：
[1] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
来源：
2019 IEEE SYMPOSIUM ON PRODUCT COMPLIANCE ENGINEERING - ASIA 2019 (IEEE ISPCE-CN 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
Material recognition; Pulsed Time-of-Flight (ToF) camera; Bidirectional Reflectance Distribution Function (BRDF); Radial basis function (RBF) neural network; DISCRIMINATIVE ILLUMINATION; OPTIMAL PROJECTIONS; CLASSIFICATION; APPROXIMATION;
D O I：
10.1109/ispce-cn48734.2019.8958633
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This study presents a method for material recognition using a pulsed time-of-flight (ToF) camera. The method measures the material bidirectional reflectance distribution function (BRDF) as a feature for material recognition by a pulsed ToF camera. We use the measurements of incident light at different angles to form the BRDF feature vectors. The feature vectors are used to build a training and test set to train and validate a classifier to perform the recognition. We choose the radial basis function (RBF) neural network as a classifier based on the nonlinear characteristics of material BRDF. Finally, we construct a turntable-based measurement system and use the material BRDF as the feature for classifying a variety of materials including metals and plastics. The optimized RBF neural network can achieve a recognition accuracy of 94.6%.
引用
收藏
页码：36 / 43
页数：8
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