Abstract
Shading analysis of an object under near lighting is not an easy task, because the direction and distance of the light source vary over the surface of the object. Observing a small area on the surface, however, techniques assuming far lighting are applicable, because variations of the direction and distance are small in the area. In this paper, we present two contributions to image segmentation for approximating object’s appearance under near light sources. First, we experimentally evaluate the accuracy of approximations using rectangular segmentation for images of objects under near light sources, and confirm the effects of image segmentation itself. Second, we propose a novel segmentation method for approximating images under near light sources. Our proposed method plans appropriate segmentations in terms of approximation accuracy, considering properties of objects and variable illumination conditions.
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References
Annen, T., Kautz, J., Durand, F., Seidel, H.-P.: Spherical harmonic gradients for mid-range illumination. In: Proc. Eurographics Sympo. Rendering 2004, pp. 331–336 (2004)
Basri, R., Jacobs, D.: Lambertian reflectance and linear subspaces. IEEE Trans. PAMI 25(2), 218–233 (2003)
Batur, A., Hayes, M.: Linear subspaces for illumination robust face recognition. In: Proc. IEEE CVPR 2001, vol. II, pp. 296–301 (2001)
Belhumeur, P., Kriegman, D.: What is the set of images of an object under all possible lighting conditions? Int’l. J. Computer Vision 28(3), 245–260 (1998)
Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. PAMI 25(9), 1063–1074 (2003)
Clark, J.: Active photometric stereo. In: Proc. IEEE CVPR 1992, pp. 29–34 (1992)
Du, F., Okabe, T., Sato, Y., Sugimoto, A.: Reflectance estimation from motion under complex illumination. In: Proc. IAPR ICPR 2004, pp. 218–222 (2004)
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley & Sons, Chichester (2001)
Frolova, D., Simakov, D., Basri, R.: Accuracy of spherical harmonics approximations for images of Lambertian objects under far and near lighting. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 574–587. Springer, Heidelberg (2004)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. PAMI 23(6), 643–660 (2001)
Iwahori, Y., Sugie, H., Ishii, N.: Reconstructing shape from shading images under point light source illumination. In: Proc. IEEE ICPR 1990, pp. I-83–87 (1990)
Kim, B., Burger, P.: Depth and shape from shading using the photometric stereo method. CVGIP: Image Understanding 54(3), 416–427 (1991)
Marschner, A., Greenberg, D.: Inverse lighting for photography. In: Fifth Color Imaging Conference, pp. 262–265 (1997)
Okabe, T., Sato, I., Sato, Y.: Spherical harmonics vs. Haar wavelets: basis for recovering illumination from cast shadows. In: Proc. IEEE CVPR 2004, pp. I–50–57 (2004)
Okabe, T., Sato, Y.: Object recognition based on photometric alignment using RANSAC. In: Proc. IEEE CVPR 2003, pp. I–221–228 (2003)
Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes in C. Cambridge University Press, Cambridge (1992)
Ramamoorthi, R., Hanrahan, P.: On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. J. Opt. Soc. Am. A 18(10), 2448–2459 (2001)
Ramamoorthi, R., Hanrahan, P.: A signal-processing framework for inverse rendering. In: Proc. ACM SIGGRAPH 2001, pp. 117–128 (2001)
Ramamoorthi, R., Hanrahan, P.: An efficient representation for irradiance environment maps. In: Proc. ACM SIGGRAPH 2001, pp. 497–500 (2001)
Sakaue, F., Shakunaga, T.: Face recognition by parallel partial projections. In: Proc. ACCV 2004, pp. 144–150 (2004)
Sato, I., Okabe, T., Sato, Y., Ikeuchi, K.: Appearance sampling for obtaining a set of basis images for variable illumination. In: Proc. IEEE ICCV 2003, pp. 800–807 (2003)
Shashua, A.: On photometric issues in 3D visual recognition from a single 2D image. Int’l. J. Computer Vision 21(1/2), 99–122 (1997)
Shum, H.-Y., Ikeuchi, K., Reddy, R.: Principal component analysis with missing data and its application to polyhedral object modeling. IEEE Trans. PAMI 17(9), 854–867 (1995)
Simakov, D., Frolova, D., Basri, R.: Dense shape reconstruction of a moving object under arbitrary, unknown lighting. In: Proc. IEEE ICCV 2003, pp. 1202–1209 (2003)
Sloan, P., Kautz, J., Snyder, J.: Precomputed radiance transfer for real-time rendering in dynamic, low frequency lighting environments. In: Proc. ACM SIGGRAPH 2002, pp. 527–536 (2002)
Zhao, L., Yang, Y.-H.: Theoretical analysis of illumination in PCA-based vision systems. Pattern Recognition 32(4), 547–564 (1999)
Zhao, L., Yang, Y.-H.: Mosaic image method: a local and global method. Pattern Recognition 32(8), 1421–1433 (1999)
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Okabe, T., Sato, Y. (2006). Effects of Image Segmentation for Approximating Object Appearance Under Near Lighting. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_77
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DOI: https://doi.org/10.1007/11612032_77
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