Color Subspaces as Photometric Invariants

• Ahmed, A., & Farag, A. (2006). A new formulation for shape from shading for non-lambertian surfaces. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 1817–1824).

• Bakshi, S., & Yang, Y.-H. (1994). Shape from shading for non-lambertian surfaces. In Proceedings of IEEE international conference on image processing (Vol. 2, pp. 130–134).

• Barnard, K., Cardei, V., & Funt, B. (2002a). A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(9).

• Barnard, K., Martin, L., Coath, A., & Funt, B. (2002b). A comparison of computational color constancy algorithms. II. Experiments with image data. IEEE Transactions on Image Processing, 11(9), 985–996.

  Article  Google Scholar 

• Barron, J. L., & Klette, R. (2002). Quantitative color optical flow. In Proceedings international conference on pattern recognition (Vol. 4, pp. 251–255). Washington: IEEE Computer Society.

  Google Scholar 

• Barsky, S., & Petrou, M. (2001). Colour photometric stereo: Simultaneous reconstruction of local gradient and colour of rough textured surfaces. In Proceedings of IEEE international conference on computer vision (pp. 600–605).

• Barsky, S., & Petrou, M. (2003). The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1239–1252.

  Article  Google Scholar 

• Bhat, D., & Nayar, S. (1998). Stereo and specular reflection. International Journal of Computer Vision, 26(2), 91–106.

  Article  Google Scholar 

• Birchfield, S., & Tomasi, C. (1998). Depth discontinuities by pixel-to-pixel stereo. In Proceedings of IEEE international conference on computer vision (pp. 1073–1080).

• Black, M. J., & Anandan, P. (1993). A framework for the robust estimation of optical flow. In Proceedings of IEEE international conference on computer vision (pp. 231–236).

• Blanz, V., & Vetter, T. (2003). Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9).

• Boykov, Y., Veksler, O., & Zabih, R. (1998). Markov random fields with efficient approximations. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 648–655).

• Brainard, D. H., & Freeman, W. T. (1997). Bayesian color constancy. Journal of Optical Society of America A, 14, 1393–1411.

  Article  Google Scholar 

• Brelstaff, G., & Blake, A. (1988). Detecting specular reflection using lambertian constraints. In Proceedings of IEEE international conference on computer vision (pp. 297–302).

• Coleman, E., & Jain, R. (1982). Obtaining 3-dimensional shape of textured and specular surfaces using four-source photometry. Computer Vision, Graphics and Image Processing, 18(4), 309–328.

  Article  Google Scholar 

• Davis, J., Yang, R., & Wang, L. (2005a). BRDF invariant stereo using light transport constancy. In Proceedings of IEEE international conference on computer vision (Vol. 1).

• Davis, J. E., Yang, R., & Wang, L. (2005b). BRDF invariant stereo using light transport constancy. In ICCV ’05: Proceedings of the tenth IEEE international conference on computer vision (ICCV’05) (Vol. 1, pp. 436–443). Washington: IEEE Computer Society.

  Chapter  Google Scholar 

• Finlayson, G. D. (1996). Color in perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 1034–1036.

  Article  Google Scholar 

• Finlayson, G., & Schaefer, G. (2001). Constrained dichromatic colour constancy. In Proceedings of European conference on computer vision (Vol. 1, pp. 342–358).

• Finlayson, G. D., Hordley, S. D., & Hubel, P. M. (2001). Color by correlation: A simple, unifying framework for color constancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 1209–1221.

  Article  Google Scholar 

• Gershon, R. (1987). The use of color in computational vision. Ph.D. thesis, University of Toronto.

• Grossberg, M. D., & Nayar, S. K. (2003). High dynamic range from multiple images: Which exposures to combine? In Proceedings of IEEE workshop on color and photometric methods in computer vision (CPMCV).

• Healey, G. (1989). Using color for geometry-insensitive segmentation. Journal of Optical Society of America A, 6(6), 920–937.

  MathSciNet  Google Scholar 

• Hertzmann, A., & Seitz, S. (2003). Shape and material by example: a photometric stereo approach. In Proceedings of IEEE conference on computer vision and pattern recognition.

• Hordley, S., & Finlayson, G. (2006). Reevaluation of color constancy algorithm performance. Journal of Optical Society of America A, 23(5), 1008–1020.

  Article  Google Scholar 

• Hordley, S. D., Finlayson, G. D., & Drew, M. S. (2002). Removing shadows from images. In Proceedings of European conference on computer vision (pp. 823–836).

• Ikeuchi, K. (1981). Determining surface orientations of specular surfaces by using the photometric stereo method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(6), 661–669.

  Article  Google Scholar 

• Jin, H., Cremers, D., Yezzi, A., & Soatto, S. (2004). Shedding light on stereoscopic segmentation. In Proceedings of IEEE conference on computer vision and pattern recognition.

• Jin, H., Soatto, S., & Yezzi, A. J. (2005). Multi-view stereo reconstruction of dense shape and complex appearance. International Journal of Computer Vision, 63(3), 175–189.

  Article  Google Scholar 

• Kim, J., Kolmogorov, V., & Zabih, R. (2003). Visual correspondence using energy minimization and mutual information.

• Klinker, G., Shafer, S., & Kanade, T. (1988). The measurement of highlights in color images. International Journal of Computer Vision, 2(1), 7–32.

  Article  Google Scholar 

• Koudelka, M., Magda, S., Belhumeur, P., & Kriegman, D. (2001). Image-based modeling and rendering of surfaces with arbitrary BRDFs. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 568–575).

• Lee, H.-S. (1986). Method for computing the scene-illuminant chromaticity from specular highlights. Journal of Optical Society of America A, 3(10), 1694–1699.

  Google Scholar 

• Lee, H. C., Breneman, E. J., & Schulte, C. P. (1990). Modeling light relfection for computer color vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(4), 402–409.

  Article  Google Scholar 

• Lehmann, T. M., & Palm, C. (2001). Color line search for illuminant estimation in real-world scenes. Journal of Optical Society of America A, 18(11), 2679–2691.

  Article  Google Scholar 

• Li, Y., Lin, S., Lu, H., Kang, S. B., & Shum, H.-Y. (2002). Multibaseline stereo in the presence of specular reflections. In ICPR ’02: Proceedings of the 16th international conference on pattern recognition (ICPR’02) (Vol. 3, p. 30573). Washington: IEEE Computer Society.

  Google Scholar 

• Lim, J., Ho, J., Yang, M.-H., & Kriegman, D. (2005). Passive photometric stereo from motion. In Proceedings of IEEE international conference on computer vision.

• Lin, S., Li, Y., Kang, S. B., Tong, X., & Shum, H.-Y. (2002). Diffuse-specular separation and depth recovery from image sequences. In ECCV ’02: proceedings of the 7th European conference on computer vision—Part III (pp. 210–224). London: Springer.

  Google Scholar 

• Lu, J., & Little, J. (1999). Reflectance and shape from images using a collinear light source. International Journal of Computer Vision, 32(3), 1–28.

  Article  Google Scholar 

• Magda, S., Kriegman, D., Zickler, T., & Belhumeur, P. (2001). Beyond Lambert: Reconstructing surfaces with arbitrary BRDFs. In Proceedings of IEEE international conference on computer vision (pp. 391–398).

• Mallick, S. P., Zickler, T. E., Belhumeur, P. N., & Kriegman, D. J. (2006). Specularity removal in images and videos: A PDE approach. In Proceedings of European conference on computer vision.

• Narasimhan, S. G., Ramesh, V., & Nayar, S. K. (2003). A class of photometric invariants: Separating material from shape and illumination. In Proceedings of IEEE international conference on computer vision (Vol. 2, pp. 1387–1394).

• Nayar, S. K., & Bolle, M. (1996). Reflectance based object recognition. International Journal of Computer Vision, 17(3), 219–240.

  Article  Google Scholar 

• Nayar, S., Ikeuchi, K., & Kanade, T. (1990). Determining shape and reflectance of hybrid surfaces by photometric sampling. IEEE Journal of Robotics and Automation, 6(4), 418–431.

  Article  Google Scholar 

• Nayar, S., Fang, X., & Boult, T. (1997). Separation of reflection components using color and polarization. International Journal of Computer Vision, 21(3), 163–186.

  Article  Google Scholar 

• Park, J. B. (2003). Efficient color representation for image segmentation under nonwhite illumination. In SPIE (Vol. 5267, pp. 163–174).

• Ragheb, H., & Hancock, E. (2001). Separating lambertian and specular reflectance components using iterated conditional modes. In Proceedings of British machine vision conference (pp. 541–522).

• Rosenberg, C., Hebert, M., & Thrun, S. (2001). Color constancy using KL-divergence. In Proceedings of IEEE international conference on computer vision (pp. 239–247).

• Sapiro, G. (1999). Color and illumination voting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 1210–1215.

  Article  Google Scholar 

• Sato, Y., & Ikeutchi, K. (1994). Temporal-color space analysis of reflection. Journal of Optical Society of America A, 11(11), 2990–3002.

  Google Scholar 

• Schlüns, K., & Wittig, O. (1993). Photometric stereo for non-Lambertian surfaces using color information. In Proceedings of international conference on image analysis and processing (pp. 505–512).

• Shafer, S. (1985). Using color to separate reflection components. COLOR Research and Applications, 10(4), 210–218.

  Article  Google Scholar 

• Silver, W. (1980). Determining shape and reflectance using multiple images. Master’s thesis, MIT.

• Tagare, H., & deFigueiredo, R. (1991). A theory of photometric stereo for a class of diffuse non-lambertian surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(2), 133–152.

  Article  Google Scholar 

• Tan, P., Lin, S., & Quan, L. (2006). Separation of highlight reflections on textured surfaces. In Proceedings of IEEE conference on computer vision and pattern recognition.

• Tan, R. T., & Ikeuchi, K. (2003). Separating reflection components of textured surface using a single image. In Proceedings of IEEE international conference on computer vision (pp. 870–877).

• Tan, R. T., Nishino, K., & Ikeutchi, K. (2004). Color constancy through inverse-intensity chromaticity space. Journal of Optical Society of America A, 21(3), 321–334.

  Article  Google Scholar 

• Tian, Y., & Tsui, H. (1997). Shape recovery from a color image for non-lambertian surfaces. Journal of Optical Society of America A, 14(2), 397–404.

  Article  Google Scholar 

• Tominga, S., & Wandell, B. (1989). Standard surface-reflectance model and illuminant estimation. Journal of Optical Society of America A, 6(4), 576–584.

  Google Scholar 

• Tominga, S., & Wandell, B. A. (2002). Natural scene-illuminant estimation using sensor correlation. Proceedings of IEEE, 90, 42–56.

  Article  Google Scholar 

• Tsumura, N., Ojima, N., Sato, K., Shiraishi, M., Shimizu, H., Nabeshima, H., Akazaki, S., Hori, K., & Miyake, Y. (2003). Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin. In Proceedings of ACM SIGGRAPH (pp. 770–779).

• Tu, P., & Mendonça, P. (2003). Surface reconstruction via Helmholtz reciprocity with a single image pair. In Proceedings of IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 541–547).

• van de Weijer, J., & Gevers, T. (2004). Robust optical flow from photometric invariants. In Proceedings of IEEE international conference on image processing (pp. 1835–1838).

• Wann Jensen, H., Marschner, S., Levoy, M., & Hanrahan, P. (2001). A practical model for subsurface light transport. In Proceedings of ACM SIGGRAPH (pp. 511–518).

• Wolff, L., & Angelopoulou, E. (1994). Three-dimensional stereo by photometric ratios. Journal of Optical Society of America A, 11, 3069–3078.

  Google Scholar 

• Wolff, L. B., & Boult, T. E. (1991). Constraining object features using a polarization reflectance model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(7), 635–657.

  Article  Google Scholar 

• Woodham, R. (1978). Photometric stereo: A reflectance map technique for determining surface orientation from image intesity. In Proceedings of SPIE (Vol. 155, pp. 136–143).

• Yang, R., Pollefeys, M., & Welch, G. (2003). Dealing with textureless regions and specular highlights-a progressive space carving scheme using a novel photo-consistency measure. In ICCV ’03: Proceedings of the ninth IEEE international conference on computer vision (p. 576). Washington: IEEE Computer Society.

  Chapter  Google Scholar 

• Yoon, K., & Kweon, I. (2006a). Correspondence search in the presence of specular highlights using specular-free two-band images. In Proceedings of Asian conference on computer vision (pp. 761–770).

• Yoon, K.-J., & Kweon, I. S. (2006b). Adaptive support-weight approach for correspondence search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 650–656.

  Article  Google Scholar 

• Zhang, L., Curless, B., Hertzmann, A., & Seitz, S. M. (2003). Shape and motion under varying illumination: Unifying structure from motion, photometric stereo, and multi-view stereo. In Proceedings of IEEE international conference on computer vision (pp. 618–625).

• Zheng, Q., & Chellappa, R. (1991). Estimation of illuminant direction, albedo, and shape from shading. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(7), 680–702.

  Article  Google Scholar 

• Zickler, T., Belhumeur, P., & Kriegman, D. (2002). Helmholtz stereopsis: Exploiting reciprocity for surface reconstruction. In Proceedings of European conference on computer vision (pp. 869–884).

• Zickler, T. E., Ho, J., Kriegman, D. J., Ponce, J., & Belhumeur, P. N. (2003). Binocular helmholtz stereopsis. In Proceedings of IEEE international conference on computer vision (p. 1411). Washington: IEEE Computer Society.

  Chapter  Google Scholar 

Download references