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JACIII Vol.14 No.1 pp. 99-109
doi: 10.20965/jaciii.2010.p0099
(2010)

Paper:

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Relative Magnitude of Gaussian Curvature via Self-Calibration

Yi Ding*1, Yuji Iwahori*2, Takashi Nakagawa*2, Tsuyoshi Nakamura*1, Lifeng He*3, Robert J. Woodham*4, and Hidenori Itoh*1

*1Nagoya Institute of Technology, Gokiso-cho Showa-ku Nagoya 466-8555, Japan

*2Chubu University, Matsumoto-cho 1200 Kasugai 487-8501, Japan

*3Faculty of Information Science and Technology, Aichi Prefectural University, Aichi 480-1198, Japan

*4University of British Columbia, Vancouver, B.C. Canada, V6T 1Z4

Received:
May 4, 2009
Accepted:
August 27, 2009
Published:
January 20, 2010
Creative Commons License
Keywords:
Gaussian curvature, photometric stereo, self-calibration, neural network
Abstract
Gaussian curvature encodes important information about object shape. This paper presents a technique to recover the relative magnitude of Gaussian curvature from multiple images acquired under different conditions of illumination. Previous approaches make use of a separate calibration sphere. Here, we require no distinct calibration object. The novel idea is to use controlled motion of the target object itself for self-calibration. The target object is rotated in fixed steps in both the vertical and the horizontal directions. A distinguished point on the object serves as a marker. Neural network training data are obtained from the predicted geometric positions of the marker under known rotations. Four light sources with different directions are used. An RBF neural network learns the mapping of image intensities to marker position coordinates along a virtual sphere. Neural network maps four image irradiances on the target object onto a point on a virtual sphere. The area value surrounded by four mapped points onto a sphere gives an approximate value of Gaussian curvature. The modification neural network is learned for the basis function to obtain more accurate Gaussian curvature. Spatially varying albedo is allowed since the effect of albedo can be removed. It is shown that self-calibration makes it possible to recover the relative magnitude of Gaussian curvature at each point without a separate calibration object. No particular functional model of surface reflectance is assumed. Experiments with real data are demonstrated. Quantitative error analysis is provided for a synthetic example.
Cite this article as:
Y. Ding, Y. Iwahori, T. Nakagawa, T. Nakamura, L. He, R. Woodham, and H. Itoh, “Relative Magnitude of Gaussian Curvature via Self-Calibration,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.1, pp. 99-109, 2010.
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References
  1. [1] R. J. Woodham, “Gradient and curvature from the photometric stereo method, including local confidence estimation,” J. of the Optical Society of America, A, Vol.11, pp. 3050-3068, 1994.
  2. [2] Y. Iwahori, R. J. Woodham, and A. Bagheri, “Principal Components Analysis and Neural Network Implementation of Photometric Stereo,” Proc. IEEEWorkshop on Physics-Based Modeling in Computer Vision, pp. 117-125, 1995.
  3. [3] Y. Iwahori, R. J. Woodham, M. Ozaki, H. Tanaka, and N. Ishii, “Neural Network Based Photometric Stereo with a Nearby Rotational Moving light Source,” IEICE Trans. on Information and Systems, Vol.E80-D, No.9, pp. 948-957, 1997.
  4. [4] E. Angelopoulou and L.B. Wolf, “Sign of Gaussian Curvature from Curve Orientation in Photometric Space,” IEEE Trans. on PAMI, Vol.20, No.10, pp. 1056-1066, 1998.
  5. [5] T. Okatani and K. Deguchi, “Determination of Sign of Gaussian Curvature of Surface from Photometric Data,” Trans. of IPSJ, Vol.39, No.5, pp. 1965-1972, 1998.
  6. [6] Y. Iwahori, S. Fukui, R. J. Woodham, and A. Iwata, “Classification of Surface Curvature from Shading Images Using Neural Network,” IEICE Trans. on Information and Systems, Vol.E81-D, No.8, pp. 889-900, 1998.
  7. [7] Y. Iwahori, S. Fukui, C. Fujitani, Y. Adachi, and R. J. Woodham, “Relative Magnitude of Gaussian Curvature from Shading Images Using Neural Network,” KES2005, Knowedge-Based Intelligent Information & Engineering Systems, LNAI 3681, pp. 813-819, 2005.
  8. [8] Y. Iwahori, T. Nakagawa, S. Fukui, H.Kawanaka, R. J. Woodham, and Y. Adachi, “Improvement of Accuracy for Gaussian Curvature Using Modification Neural Network,” Knowedge-Based Intelligent Information & Engineering Systems, KES(l), pp. 1013-1020, 2007.
  9. [9] A. Hertzmann, “Example-Based Photometric Stereo, Shape Reconstruction with General, Varying BRDFs,” IEEE Trans. on PAMI, Vol.27, No.8, pp. 1254-1264, 2005.
  10. [10] R. J.Woodham, “Photometric Method for Determining Surface Orientation from Multiple Images,” Optical Engineering, Vol.19, No.1, pp. 139-144, 1980.
  11. [11] B. T. Phong, “Illumination for Computer Generated Pictures,” Comm. ACM, Vol.18 No.6, pp. 311-317, 1975.

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