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

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%.

[1]  HoraudRadu,et al.  An overview of depth cameras and range scanners based on time-of-flight technologies , 2016 .

[2]  T. Pock,et al.  Second order total generalized variation (TGV) for MRI , 2011, Magnetic resonance in medicine.

[3]  Andrew Zisserman,et al.  A Statistical Approach to Material Classification Using Image Patch Exemplars , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Holger Schöner,et al.  Denoising techniques for raw 3D data of TOF cameras based on clustering and wavelets , 2008, Electronic Imaging.

[5]  Duke Gledhill,et al.  Surface measurement using active vision and light scattering , 2007 .

[6]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[7]  Sabine Süsstrunk,et al.  Material Classification Using Color and NIR Images , 2009, CIC.

[8]  Matthew O'Toole,et al.  Defocus deblurring and superresolution for time-of-flight depth cameras , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Ramesh Raskar,et al.  Signal Processing for Time-of-Flight Imaging Sensors: An introduction to inverse problems in computational 3-D imaging , 2016, IEEE Signal Processing Magazine.

[10]  Ko Nishino,et al.  Visual Material Traits: Recognizing Per-Pixel Material Context , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[11]  S. Foix,et al.  Lock-in Time-of-Flight (ToF) Cameras: A Survey , 2011, IEEE Sensors Journal.

[12]  Barbara Caputo,et al.  Class-Specific Material Categorisation , 2005, ICCV.

[13]  Hong Chen,et al.  Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks , 1993, IEEE Trans. Neural Networks.

[14]  Hang Zhang,et al.  Reflectance hashing for material recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  James J. Carroll,et al.  Approximation of nonlinear systems with radial basis function neural networks , 2001, IEEE Trans. Neural Networks.

[16]  Ramesh Raskar,et al.  Coded time of flight cameras , 2013, ACM Trans. Graph..

[17]  T. Fearn,et al.  Near infrared spectroscopy in food analysis , 1986 .

[18]  Matthew O'Toole,et al.  Temporal frequency probing for 5D transient analysis of global light transport , 2014, ACM Trans. Graph..

[19]  Edward H. Adelson,et al.  Exploring features in a Bayesian framework for material recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Matthew O'Toole,et al.  Decomposing global light transport using time of flight imaging , 2012, CVPR.

[21]  Chein-I Chang,et al.  Robust radial basis function neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[22]  Wojciech Matusik,et al.  A data-driven reflectance model , 2003, ACM Trans. Graph..

[23]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2005, International Journal of Computer Vision.

[24]  Jerry Workman.  15 – Interpretive Spectroscopy For Near-Infrared , 2001 .

[25]  Ramesh Raskar,et al.  Occluded Imaging with Time-of-Flight Sensors , 2016, ACM Trans. Graph..

[26]  Tomohiro Tanikawa,et al.  SpecTrans: Versatile Material Classification for Interaction with Textureless, Specular and Transparent Surfaces , 2015, CHI.

[27]  Chao Liu,et al.  Discriminative illumination: Per-pixel classification of raw materials based on optimal projections of spectral BRDF , 2012, CVPR.

[28]  Yasushi Yagi,et al.  Material Classification Using Frequency-and Depth-Dependent Time-of-Flight Distortion , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Paolo Frasconi,et al.  Learning without local minima in radial basis function networks , 1995, IEEE Trans. Neural Networks.

[30]  Chao Liu,et al.  Learning Discriminative Illumination and Filters for Raw Material Classification with Optimal Projections of Bidirectional Texture Functions , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Lena Maier-Hein,et al.  Adaptive bilateral filter for image denoising and its application to in-vitro Time-of-Flight data , 2011, Medical Imaging.

[32]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[33]  Qionghai Dai,et al.  Fourier Analysis on Transient Imaging with a Multifrequency Time-of-Flight Camera , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Reinhard Klein,et al.  Solving trigonometric moment problems for fast transient imaging , 2015, ACM Trans. Graph..

[35]  Ko Nishino,et al.  Automatically discovering local visual material attributes , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  A. B. Orun,et al.  Material identification by surface reflection analysis in combination with bundle adjustment technique , 2003, Pattern Recognit. Lett..

[37]  J. Mark.  Introduction to radial basis function networks , 1996 .

[38]  Oliver Wang,et al.  Material classification using BRDF slices , 2009, CVPR.