Ball, G. and Hall, D. 1967. A clustering technique for summarizing multi-variate data. Behavioral Science
, 12:153-155.Google Scholar
Belhumeur, P. and Kriegman, D. 1998. What is the set of images of an object under all possible illumination conditions?. International Journal of Computer Vision
, 28(3):245-260.Google Scholar
Burt, P. and Adelson, E. 1983. The laplacian pyramid as a compact image code. IEEE Transactions on Communications
, 31(4):532-540.Google Scholar
Chantler, M. 1994. Towards illuminant invariant texture classification. In Proc. IEE Coll. on Texture Classification: Theory and Applications.
Chantler, M. and McGunnigle, G. 1995. Compensation of illuminant tilt variation for texture classification. In Proceedings Fifth International Conference on Image Processing and its Applications, pp. 767-771.
Chellappa, R. and Chatterjee, S. 1985. Classification of textures using Gaussian Markov random fields. IEEE Transactions on Acoustics, Speech, Signal Processing
, 33(4):959-963.Google Scholar
Cross, G. and Jain, A. 1983. Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence
, 5(1):25-39.Google Scholar
Dana, K. and Nayar, S. 1998. Histogram model for 3D textures. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, pp. 618-624.
Dana, K. and Nayar, S. 1999a. 3D textured surface modelling. In Proceedings Workshop on the Integration of Appearance and Geometric Methods in Object Recognition, pp. 46-56.
Dana, K. and Nayar, S. 1999b. Correlation model for 3D texture. In Proceedings IEEE 7th International Conference on Computer Vision
, Vol. 2. Corfu, Greece, pp. 1061-1066.Google Scholar
Dana, K., van Ginneken, B., Nayar, S., and Koenderink, J. 1999. Reflectance and texture of real-world surfaces. ACMTransactions on Graphics
, 18(1):1-34.Google Scholar
de Bonet, J. and Viola, P. 1998. Texture recognition using a nonparametric multi-scale statistical model. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, pp. 641-647.
Duda, R. and Hart, P. 1973. Pattern Classification and Scene Analysis
, John Wiley & Sons. New York, N.Y.Google Scholar
Efros, A. and Leung, T. 1999. Texture synthesis by non-parametric sampling. In Proceedings IEEE 7th International Conference on Computer Vision
, Vol. 2. Corfu, Greece, pp. 1033-1038.Google Scholar
Fogel, I. and Sagi, D. 1989. Gabor filters as texture discriminator. Biological Cybernetics
, 61:103-113.Google Scholar
Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence
, 6:721-741.Google Scholar
Georghiades, A., Kriegman, D., and Belhumeur, P. 1998. Illumination cones for recognition under variable lightin: Faces. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, pp. 52-58.
Gersho, A. and Gray, R. 1992. Vector Quantization and Signal Compression
, Kluwer Academic Publishers: Boston, MA.Google Scholar
Gilks, W., Richardson, S., and Spiegelhalter, D. 1996. Markov Chain Monte Carlo in Practice, Chapman and Hall.
Haddon, J. and Forsyth, D. 1998. Shading primitives: Finding folds and shallow grooves. In Proceedings IEEE 6th International Conference on Computer Vision, Bombay, India, pp. 236-241.
Heeger, D. and Bergen, J. 1995. Pyramid-based texture analysis/ synthesis. In Computer Graphics (SIGGRAPH '95 Proceedings), Los Angeles, CA, pp. 229-238.
Jain, A. and Farrokhsia, F. 1991. Unsupervised texture segmentation using Gabor filters. Pattern Recognition
, 24:1167-1186.Google Scholar
Jones, D. and Malik, J. 1992. Computational framework to determining stereo correspondence from a set of linear spatial filters. Image and Vision Computing
, 10(10):699-708.Google Scholar
Julesz, B. 1981. Textons, the elements of texture perception, and their interactions. Nature
, 290(5802):91-97.Google Scholar
Koenderink, J. and van Doorn, A. 1980. Photometric invariants related to solid shape. Optica Acta
, 27(7):981-996.Google Scholar
Koenderink, J. and van Doorn, A. 1996. Illuminance texture due to surface mesostructure. Journal of the Optical Society America A
, 13(3):452-463.Google Scholar
Koenderink, J., van Doorn, A. Dana, K. and Nayar, S. 1999. Bidirectional reflection distribution function of thoroughly pitted surfaces. International Journal of Computer Vision
, 31(2/3):129-144.Google Scholar
Leung, T. and Malik, J. 1997. On perpendicular texture or: Why dowe see more flowers in the distance?. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 807-813.
Leung, T. and Malik, J. 1999. Recognizing surfaces using three dimensional textons. In Proc. IEEE International Conference on Computer Vision, Corfu, Greece.
MacQueen, J. 1967. Some methods for classification and analysis of multivariate observations. In Proc. Fifth Berkeley Symposium on Math. Stat. and Prob.
, Vol. I. pp. 281-297.Google Scholar
Malik, J., Belongie, S., Shi, J., and Leung, T. 1999. Textons, contours and regions: Cue integration in image segmentation. In Proceedings IEEE 7th International Conference on Computer Vision, Corfu, Greece, pp. 918-925.
Malik, J. and Perona, P. 1990. Preattentive texture discrimination with early vision mechanisms. Journal of the Optical Society of America A
, 7(5):923-932.Google Scholar
Mao, J. and Jain, A. 1992. Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition
, 25(2):173-188.Google Scholar
Murase, H. and Nayar, S. 1995. Visual learning and recognition of 3-D objects from appearance. International Journal on Computer Vision
, 14(1):5-24.Google Scholar
Press, W., Flannery, B., Teukolsky, S., and Vetterling, W. 1988. Numerical Recipes in C, Cambridge University Press.
Puzicha, J., Hofmann, T., and Buhmann, J. 1997. Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 267-272.
Ripley, B. 1996. Pattern Recognition and Neural Networks, Cambridge University Press.
Rubner, Y. and Tomasi, C. 1999. Texture-based image retrieval without segmentation. In Proceedings IEEE 7th International Conference on Computer Vision
, Vol. 2. Corfu, Greece, pp. 1018-1024.Google Scholar
Sebestyen, G. 1962. Pattern recognition by an adaptive process of sample set construction. IRE Trans. Info. Theory
, 8:S82-S91.Google Scholar
Shashua, A. 1997. On photometric issues in 3D visual recognition from a single 2D image. International Journal on Computer Vision, 21(1/2).
Sirovitch, L. and Kirby, M. 1987. Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A
, 2:519-524.Google Scholar
Turk, M. and Pentland, A. 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience
, 3(1):71-86.Google Scholar
Vaidyanathan, P. 1993. Multirate Systems and Filter Banks
, Prentice-Hall: Englewood Cliffs, N.J.Google Scholar
van Ginneken, B., Stavridi, M., and Koenderink, J. 1998. Diffuse and specular reflectance from rough surfaces. Applied Optics
, 37(1):130-139.Google Scholar
Yuan, J. and Rao, S. 1993. Spectral estimation for random fields with applications to Markov modeling and texture classification. In Markov Random Fields: Theory and Application, R. Chellappa and A. Jain (Eds.). Academic Press.
Zhu, S., Wu, Y., and Mumford, D. 1998. Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling. International Journal of Computer Vision
, 27(2):107-126.Google Scholar