2009. 7. 3. 15:18
Computer Vision
[1] R. Woodham, “Photometric method for determining surface orientation
from multiple images,” Optical Engineering, vol. 19, no. 1,
pp. 139–144, 1980.
[2] S. Shafer, “Using color to separate reflection components,” Color
Research and Applicatios, vol. 10, no. 4, pp. 210–218, 1985.
[3] G. Klinker, S. Shafer, and T. Kanade, “The measurement of highlights
in color images,” International Journal of Computer Vision,
vol. 2, no. 1, pp. 7–32, 1988.
[4] Y. Sato, M. Wheeler, and K. Ikeuchi, “Object shape and reflectance
modeling from observation,” in SIGGRAPH, 1997.
[5] S. Mallick, T. Zickler, D. Kriegman, and P. Bellhumeur, “Beyond
Lambert: Reconstructing specular surfaces using color,” in Proc.
IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[6] Y. Sato and K. Ikeuchi, “Temporal-color space analysis of reflection,”
CMU, Tech. Rep. CMU-CS-92-207, 1992.
[7] M. Chandraker, S. Agarwal, and D. Kriegman, “Shadowcuts:
Photometric stereo with shadows,” in Proc. IEEE Conf. Computer
Vision and Pattern Recognition, 2007.
[8] W. Silver, “Determining shape and reflectance using multiple
images,” Master’s thesis, MIT, 1980.
[9] A. Hertzmann and S. Seitz, “Shape and materials by example: A
photometric stereo approach,” in Proc. IEEE Conf. Computer Vision
and Pattern Recognition, 2003.
[10] D. Goldman, “Shape and spatially-varying BRDFs from photometric
stereo,” in Proc. Int. Conf. on Computer Vision, 2005.
[11] H.-S. Chung and J. Jia, “Efficient photometric stereo on glossy
surfaces with wide specular lobes,” in Computer Vision and Pattern
Recognition, IEEE Computer Society Conference on, 2008.
[12] T. Zickler, P. Belhumeur, and D. Kriegman, “Helmholtz stereopsis:
Exploiting reciprocity for surface reconstruction,” in Proc. European
Conf. on Computer Vision, 2002.
[13] N. G. Alldrin and D. J. Kriegman, “Toward reconstructing surfaces
with arbitrary isotropic reflectance : A stratified photometric
stereo approach,” in Proc. Int. Conf. on Computer Vision, 2007.
[14] N. G. Alldrin, T. Zickler, and D. J. Kriegman, “Photometric stereo
with non-parametric and spatially-varying reflectance,” in Proc.
IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[15] R. Fletcher, Practical Methods of Optimization. John WIley & Sons,
1987.
[16] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge
Press, 2004.
[17] R. I. Hartley and F. Kahl, “Global optimization through rotation
space search,” Int. J. Computer Vision, vol. 82, pp. 64–79, 2009.
[18] W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical
Recipes in C: The Art of Scientiic Computing. Cambridge University
Press, 1992.
[19] S. Seitz, “Computer vision project: Photometric stereo,” 2005,
http://www.cs.washington.edu/education/courses/csep576/
05wi/projects/project3/project3.htm.
from multiple images,” Optical Engineering, vol. 19, no. 1,
pp. 139–144, 1980.
[2] S. Shafer, “Using color to separate reflection components,” Color
Research and Applicatios, vol. 10, no. 4, pp. 210–218, 1985.
[3] G. Klinker, S. Shafer, and T. Kanade, “The measurement of highlights
in color images,” International Journal of Computer Vision,
vol. 2, no. 1, pp. 7–32, 1988.
[4] Y. Sato, M. Wheeler, and K. Ikeuchi, “Object shape and reflectance
modeling from observation,” in SIGGRAPH, 1997.
[5] S. Mallick, T. Zickler, D. Kriegman, and P. Bellhumeur, “Beyond
Lambert: Reconstructing specular surfaces using color,” in Proc.
IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[6] Y. Sato and K. Ikeuchi, “Temporal-color space analysis of reflection,”
CMU, Tech. Rep. CMU-CS-92-207, 1992.
[7] M. Chandraker, S. Agarwal, and D. Kriegman, “Shadowcuts:
Photometric stereo with shadows,” in Proc. IEEE Conf. Computer
Vision and Pattern Recognition, 2007.
[8] W. Silver, “Determining shape and reflectance using multiple
images,” Master’s thesis, MIT, 1980.
[9] A. Hertzmann and S. Seitz, “Shape and materials by example: A
photometric stereo approach,” in Proc. IEEE Conf. Computer Vision
and Pattern Recognition, 2003.
[10] D. Goldman, “Shape and spatially-varying BRDFs from photometric
stereo,” in Proc. Int. Conf. on Computer Vision, 2005.
[11] H.-S. Chung and J. Jia, “Efficient photometric stereo on glossy
surfaces with wide specular lobes,” in Computer Vision and Pattern
Recognition, IEEE Computer Society Conference on, 2008.
[12] T. Zickler, P. Belhumeur, and D. Kriegman, “Helmholtz stereopsis:
Exploiting reciprocity for surface reconstruction,” in Proc. European
Conf. on Computer Vision, 2002.
[13] N. G. Alldrin and D. J. Kriegman, “Toward reconstructing surfaces
with arbitrary isotropic reflectance : A stratified photometric
stereo approach,” in Proc. Int. Conf. on Computer Vision, 2007.
[14] N. G. Alldrin, T. Zickler, and D. J. Kriegman, “Photometric stereo
with non-parametric and spatially-varying reflectance,” in Proc.
IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[15] R. Fletcher, Practical Methods of Optimization. John WIley & Sons,
1987.
[16] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge
Press, 2004.
[17] R. I. Hartley and F. Kahl, “Global optimization through rotation
space search,” Int. J. Computer Vision, vol. 82, pp. 64–79, 2009.
[18] W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical
Recipes in C: The Art of Scientiic Computing. Cambridge University
Press, 1992.
[19] S. Seitz, “Computer vision project: Photometric stereo,” 2005,
http://www.cs.washington.edu/education/courses/csep576/
05wi/projects/project3/project3.htm.
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