The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - Jan. (2014 vol.36)
pp: 86-98
Chao Liu , Center for Imaging Sci., Rochester Inst. of Technol., Rochester, NY, USA
Jinwei Gu , Center for Imaging Sci., Rochester Inst. of Technol., Rochester, NY, USA
ABSTRACT
Classifying raw, unpainted materials--metal, plastic, ceramic, fabric, and so on--is an important yet challenging task for computer vision. Previous works measure subsets of surface spectral reflectance as features for classification. However, acquiring the full spectral reflectance is time consuming and error-prone. In this paper, we propose to use coded illumination to directly measure discriminative features for material classification. Optimal illumination patterns--which we call "discriminative illumination"--are learned from training samples, after projecting to which the spectral reflectance of different materials are maximally separated. This projection is automatically realized by the integration of incident light for surface reflection. While a single discriminative illumination is capable of linear, two-class classification, we show that multiple discriminative illuminations can be used for nonlinear and multiclass classification. We also show theoretically that the proposed method has higher signal-to-noise ratio than previous methods due to light multiplexing. Finally, we construct an LED-based multispectral dome and use the discriminative illumination method for classifying a variety of raw materials, including metal (aluminum, alloy, steel, stainless steel, brass, and copper), plastic, ceramic, fabric, and wood. Experimental results demonstrate its effectiveness.
INDEX TERMS
material classification, Computational illumination, appearance modeling,
CITATION
Chao Liu, Jinwei Gu, "Discriminative Illumination: Per-Pixel Classification of Raw Materials Based on Optimal Projections of Spectral BRDF", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 1, pp. 86-98, Jan. 2014, doi:10.1109/TPAMI.2013.110
REFERENCES
[1] E.H. Adelson and J.R. Bergen, "The Plenoptic Function and the Elements of Early Vision," Computational Models of Visual Processing, pp. 3-20, MIT Press, 1991.
[2] A. Gesing, "Assuring Continued Recyclability of Automotive Aluminum Alloys: Chemical-Composition-Based Sorting of Wrought and Cast Al Shred," Proc. TMS Ann. Meeting: Automotive Alloys, 2003.
[3] P.N. Belhumeur, J. ao, P. Hespanha, and D.J. Kriegman, "Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[4] H. Chen and L.B. Wolff, "Polarization Phase-Based Method for Material Classification in Computer Vision," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 73-83, June 1998.
[5] O.G. Cula and K.J. Dana, "3D Texture Recognition Using Bidirectional Feature Histograms," Int'l J. Computer Vision, vol. 59, no. 1, pp. 33-60, Aug. 2005.
[6] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, second ed. Wiley-Interscience, 2001.
[7] R.A. Fisher, "The Use of Multiple Measurements in Taxonomic Problems," Ann. Human Genetics, vol. 7, no. 2, pp. 179-188, 1936.
[8] Y. Freund and R.E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," J. Computer and System Sciences, vol. 55, no. 1, pp. 119-139, Aug. 1997.
[9] J. Gama and P. Brazdil, "Cascade Generalization," Machine Learning, vol. 41, no. 3, pp. 315-343, Dec. 2007.
[10] A. Ghosh, W. Heidrich, S. Achutha, and M. O'Toole, "BRDF Acquisition with Basis Illumination," Proc. IEEE Int'l Conf. Computer Vision, Oct. 2007.
[11] J. Gu and C. Liu, "Discrminative Illumination: Per-Pixel Classification of Raw Materials Based Optimal Projections of Spectral BRDFs," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2012.
[12] J. Hendrik, K.A. Massey, E. Whitham, B. Bras, and M.D. Russell, "Technologies for the Identification, Separation, and Recycling of Automative Plastics," Int'l J. Environmentally Conscious Design and Manufacturing, vol. 6, no. 2, pp. 37-50, 1997.
[13] J.Y. Hwang, X. Huang, and Z. Xu, "Recovery of Metals from Aluminum Dross and Salt Cake," J. Minerals and Materials Characterization and Eng., vol. 5, pp. 47-62, 2006.
[14] M.W. Hyde, J.D. Schmidt, M.J. Havrilla, and S.C. Cain, "Enhanced Material Classification Using Turbulence-Degraded Polarimetric Imagery," Optics Letters, vol. 35, no. 21, pp. 3601-3603, Nov. 2010.
[15] M.W. Hyde, S.C. Cain, J.D. Schmidt, and M.J. Havrilla, "Material Classification of an Unknown Object Using Turbulence-Degraded Polarimetric Imagery," IEEE Trans. Geoscience and Remote Sensing, vol. 49, no. 1, pp. 264-276, Jan. 2011.
[16] A. Ibrahim, S. Tominaga, and T. Horiuchi, "Spectral Imaging Method for Material Classification and Inspection of Printed Circuit Boards," Optical Eng., vol. 49, no. 5, pp. 057201-057201-10, May 2010.
[17] M. Jehle, C. Sommer, and B. Jähne, "Learning of Optimal Illumination for Material Classification," Proc. 32nd DAGM Conf. Pattern Recognition, 2010.
[18] S.S. Khan and M.G. Madden, "A Survey of Recent Trends in One Class Classification," Proc. 20th Irish Conf. Artificial Intelligence and Cognitive Science, pp. 188-197, 2010.
[19] D. LaBelle, J. Bares, and I. Nourbakhsh, "Material Classification by Drilling," Proc. 17th Int'l Symp. Automation and Robotics in Construction, Sept. 2000.
[20] T. Leung and J. Malik, "Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons," Int'l J. Computer Vision, vol. 43, no. 1, pp. 29-44, June 2001.
[21] W.C. Ma, T. Hawkins, P. Peers, C.F. Chabert, M. Weiss, and P. Debevec, "Rapid Acquisition of Specular and Diffuse Normal Maps from Polarized Spherical Gradient Illumination," Proc. Eurographics Symp. Rendering, 2007.
[22] M.A. Mannan, D. Dipankar, K. Yoshinori, and K. Yoshinori, "Object Material Classification by Surface Reflection Analysis with a Time-of-Flight Range Sensor," Proc. Int'l Symp. Visual Computing, 2010.
[23] W. Matusik, H. Pfister, M. Brand, and L. McMillan, "A Data-Driven Reflectance Model," ACM Trans. Graphics, vol. 22, pp. 759-769, 2003.
[24] W. Matusik, H. Pfister, M. Brand, and L. McMillan, "A Data-Driven Reflectance Model," ACM Trans. Graphics, vol. 22, no. 3, pp. 759-769, July 2003.
[25] F. Melgani and L. Bruzzone, "Classification of Hyperspectral Remote Sensing Images with Support Vector Machines," IEEE Trans. Geoscience and Remote Sensing, vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
[26] S. Nayar, G. Krishnan, M.D. Grossberg, and R. Raskar, "Fast Separation of Direct and Global Components of a Scene Using High Frequency Illumination," ACM Trans. Graphics, vol. 25, pp. 935-944, 2006.
[27] M.A. Neifeld and P. Shankar, "Feature-Specific Imaging," Applied Optics, vol. 42, no. 17, pp. 3379-3389, June 2003.
[28] M.A. Neifeld, A. Ashok, and P.K. Baheti, "Task-Specific Information for Imaging System Analysis," J. Optical Soc. Am. A, vol. 24, no. 12, pp. B25-B41, Dec. 2007.
[29] A. Ngan, F. Durand, and W. Matusik, "Experimental Analysis of BRDF Models," Proc. Eurographics Symp. Rendering, pp. 117-226, 2005.
[30] F.E. Nicodemus, J.C. Richmond, J.J. Hsia, I.W. Ginsberg, and T. Limperis, "Geometrical Considerations and Nomenclature for Reflectance," Radiometry, L.B. Wolff, S.A. Shafer, and G. Healey, ed., vol. 160, pp. 94-145, Nat'l Bureau of Standards Mo nograph, Oct. 1992.
[31] K. Nishino, "Directional Statistics BRDF Model," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 476-483, 2009.
[32] A.B. Orun and A. Alkis, "Material Identification by Surface Reflection Analysis in Combination with Bundle Adjustment Technique," Pattern Recognition Letters, vol. 24, no. 9/10, pp. 1589-1598, June 2003.
[33] R. Polikar, "Ensemble Based Systems in Decision Making," IEEE Circuits and Systems Magazine, vol. 6, no. 3, pp. 21-45, Third Quarter 2006.
[34] N. Salamati, C. Fredembach, and S. Süsstrunk, "Material Classification Using Color and NIR Images," Proc. IS&T/SID 17th Color Imaging Conf., 2009.
[35] Y.Y. Schechner, S.K. Nayar, and P.N. Belhumeur, "A Theory of Multiplexed Illumination," Proc. IEEE Int'l Conf. Computer Vision, Oct. 2003.
[36] M.E. Schlesinger, O. llegbusi, M. Iguchi, and W. Wahnsiedler, Aluminum Recycling. CRC Press, 2000.
[37] D. Sun, Computer Vision Technology for Food Quality Evaluation. Academic Press, 2007.
[38] G.Y. Tian, R.S. Lu, and D. Gledhill, "Surface Measurement Using Active Vision and Light Scattering," Optics and Lasers in Eng., vol. 45, pp. 131-139, Jan. 2007.
[39] M. Varma and A. Zisserman, "A Statistical Approach to Texture Classification from Single Images," Int'l J. Computer Vision, vol. 62, no. 1/2, pp. 61-81, Apr. 2005.
[40] M. Varma and A. Zisserman, "A Statistical Approach to Material Classification Using Image Patch Exemplars," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2032-2047, Nov. 2009.
[41] P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
[42] O. Wang, P. Gunawardane, S. Scher, and J. Davis, "Material Classification Using BRDF Slices," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[43] L.B. Wolff, "Polarization-Based Material Classification from Specular Reflection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 11, pp. 1059-1071, Nov. 1990.
[44] Y.Y. Schechner, S.K. Nayar, and P.N. Belhumeur, "Multiplexing for Optimal Lighting," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 8, pp. 1339-1354, Aug. 2007.
86 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool