ToF: Materials Recognition with ToF Camera

Springer Machine Vision and Applications Journal publishes a paper “ Classification of materials using a pulsed time-of-flight camera” by ShiNan Lang, Jizhong Zhang, Yiheng Cai, Xiaoqing Zhu, and Qiang Wu from Beijing University of Technology, China.

We propose an innovative method of material classification based on the imaging model of pulsed time-of-flight (ToF) camera integrated with the unique signature that describes physical properties of each material named reflection point spread function (RPSF). First, the optimization method reduces the effect of material surface interreflection, which would affect RPSF and lead to decreased accuracy in classification, by alternating direction method of multipliers (ADMM). A method named feature vector normalization is proposed to extract material RPSF features. Second, according to the nonlinearity of the feature vectors, the structure of hidden layer neurons of radial basis function (RBF) neural network is optimized based on singular value decomposition (SVD) to improve generalization. Finally, the similar appearance of plastics and metals are classified on turntable-based measurement system by own design. The average classification accuracy reaches 93.3%, and the highest classification accuracy reaches 94.6%.

Originally published at http://image-sensors-world.blogspot.com.

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
For 4D Sensing, AR, AV/Robot, and AIoT

Marc. Y. , Tech Leader. Focus on new techs such as 4D Sensing, AR, AV/Robot, and AIoT. More at http://4da.tech/