This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
The Analysis and Recognition of Real-World Textures in Three Dimensions
May 2000 (vol. 22 no. 5)
pp. 491-503

Abstract—The observed image texture for a rough surface has a complex dependence on the illumination and viewing angles due to effects such as foreshortening, local shading, interreflections, and the shadowing and occlusion of surface elements. We introduce the dimensionality surface as a representation for the visual complexity of a material sample. The dimensionality surface defines the number of basis textures that are required to represent the observed textures for a sample as a function of ranges of illumination and viewing angles. Basis textures are represented using multiband correlation functions that consider both within and between color band correlations. We examine properties of the dimensionality surface for real materials using the Columbia Utrecht Reflectance and Texture (CUReT) database. The analysis shows that the dependence of the dimensionality surface on ranges of illumination and viewing angles is approximately linear with a slope that depends on the complexity of the sample. We extend the analysis to consider the problem of recognizing rough surfaces in color images obtained under unknown illumination and viewing geometry. We show, using a set of 12,505 images from 61 material samples, that the information captured by the multiband correlation model allows surfaces to be recognized over a wide range of conditions. We also show that the use of color information provides significant advantages for three-dimensional texture recognition.

[1] R. Bajscy and L. Lieberman, “Texture Gradient as a Depth Cue,” Computer Vision, Graphics, and Image Processing, vol. 5, pp. 52-67, 1976.
[2] L.G. Brown and H. Shvaytser, “Surface Orientation from Projective Foreshortening of Isotropic Texture Autocorrelation,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 12, pp. 584-588, 1990.
[3] Markov Random Fields, Theory and Applications, R. Chellappa and A.K. Jain, eds. San Diego, Calif.: Academic Press, 1993.
[4] K.J. Dana and S.K. Nayar, “Histogram Model for 3D Textures,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 618-624, 1998.
[5] K.J. Dana, B. van Ginneken, S.K. Nayar, and J.J. Koenderink, “Reflectance and Texture of Real-World Surfaces,” Technical Report CUCS-048-96, Columbia Univ., Dec. 1996.
[6] K.J. Dana, B. van Ginneken, S.K. Nayar, and J.J. Koenderink, “Reflectance and Texture of Real-World Surfaces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 151-157, 1997.
[7] K.J. Dana, B. van Ginneken, S.K. Nayar, and J.J. Koenderink, “Reflectance and Texture of Real-World Surfaces,” ACM Trans. Graphics, vol. 18, no. 1, pp. 1-34, Jan. 1999.
[8] J.J. Gibson, “The Perception of Visual Surfaces,” Am. J. Psychology, vol. 63, pp. 367-384, 1950.
[9] G. Golub and C. Van Loan, Matrix Computations, third ed. Baltimore: Johns Hopkins Univ. Press, 1996.
[10] R. Haralick, “Statistical and Structural Approaches to Texture,” Proc. IEEE, vol. 67, no. 5, pp. 786-804, May 1979.
[11] G. Healey and Q.-T. Luong, “Color in Computer Vision: Recent Progress,” Handbook of Pattern Recognition and Computer Vision, pp. 283-312, C.H. Chen, L.F. Pau, and P.S.P. Wang, eds., 1999.
[12] G. Healey and L. Wang, “Illumination-Invariant Recognition of Texture in Color Images,” J. Optical Soc. Am. A, vol. 12, no. 9, pp. 1,877-1,883, Sept. 1995.
[13] J.Y. Jau and R.T. Chin, “Shape from Texture Using Wigner Distribution,” Computer Vision, Graphics, and Image Processing,” vol. 52, pp. 248-263, 1990.
[14] J.J. Koenderink and A.J. van Doorn, “Illuminance Texture Due to Surface Mesostructure,” J. Optical Soc. Am. A, vol. 13, pp. 452-463, Mar. 1996.
[15] J.J. Koenderink and A.J. van Doorn, “Phenomenological Description of Bidirectional Surface Reflection,” J. Optical Soc. Am. A, vol. 15, pp. 2,903-2,912, Nov. 1998.
[16] J.J. Koenderink, A.J. van Doorn, and M. Stavridi, “Bidirectional Reflection Distribution Function Expressed in Terms of Surface Scattering Modes,” Proc. European Conf. Computer Vision, pp. 28-39, 1996.
[17] R. Kondepudy and G. Healey, “Use of Invariants for Recognition of Three-Dimensional Color Textures,” J. Optical Soc. Am. A, vol. 11, no. 11, pp. 3,037-3,049, Nov. 1994.
[18] J. Krumm and S.A. Shafer, “Texture Segmentation and Shape in the Same Image,” Proc. Int'l Conf. Computer Vision, pp. 121-127, 1995.
[19] T. Leung and J. Malik, “On Perpendicular Texture or: Why Do We See More Flowers in the Distance?” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 807-813, 1997.
[20] J. Malik and R. Rosenholtz, “A Differential Method for Computing Local Shape-from-Texture for Planar and Curved Surfaces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 267-273, 1993.
[21] S.K. Nayar and M. Oren, “Visual Appearances of Matte Surfaces,” Science, vol. 267, pp. 1,153-1,156, Feb. 1995.
[22] A.V. Oppenheim and R.W. Schafer, Discrete-Time Signal Processing.Englewood Cliffs, N.J.: Prentice Hall, 1989.
[23] M. Oren and S.K. Nayar, “Generalization of the Lambertian Model and Implications for Machine Vision,” Int'l J. Computer Vision, vol. 14, no. 3, pp. 227-251, 1995.
[24] M.A.S. Patel and F.S. Cohen, “Local Surface Shape Estimation of 3D Textured Surfaces Using Gaussian Markov Random Fields and Stereo Windows,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 15, pp. 1,091-1,098, 1993.
[25] R. Siegel and J. Howell, Thermal Radiation Heat Transfer, third ed. Washington, D.C.: Hemisphere Publishing, 1992.
[26] B.J. Super and A.C. Bovik, “Shape from Texture Using Local Spectral Moments,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 17, no. 4, pp. 333-343, Apr. 1995.
[27] K. Torrance and E. Sparrow, “Theory for Off-Specular Reflection from Roughened Surfaces,” J. Optical Soc. Am., vol. 57, pp. 1,105-1,114, 1967.
[28] L. Wolff, “A Diffuse Reflectance Model for Smooth Dielectrics,” J. Optical Soc. Am. A, vol. 11, pp. 2,956-2,968, Nov. 1994.

Index Terms:
3D texture, texture, color, recognition, classification, computer vision, bidirectional reflectance distribution function (BRDF), invariant, bidirectional texture function (BTF).
Citation:
Pei-hsiu Suen, Glenn Healey, "The Analysis and Recognition of Real-World Textures in Three Dimensions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 5, pp. 491-503, May 2000, doi:10.1109/34.857005
Usage of this product signifies your acceptance of the Terms of Use.