Abstract
This paper proposes a new approach to recover the relative magnitude of Gaussian curvature of the test object from four shading images using modified neural network. The method is expanded to an object with color texture using four shading images taken under the different light source directions. Neural network mapps four image irradiances on the test object onto a point on a sphere. The area value surrounded by four mapped points onto a sphere gives an approximate value of Gaussian curvature. To get more accurate Gaussian curvature, the modification neural network is introduced and learned for the synthesized 2-D basis function consisting of 2-D cosine function. It is shown that learnt NN gives better accuracy for the relative magnitude of Gaussian curvature of the test object.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Woodham, R.J.: Gradient and curvature from the photometric stereo method, including local confidence estimation. Journal of the Optical Society of America, A 11, 3050–3068 (1994)
Iwahori, Y., Woodham, R.J., Bagheri, A.: Principal components analysis and neural network implementation of photometric stereo. In: Proc. IEEE Workshop on Physics-Based Modeling in Computer Vision, pp. 117–125. IEEE Computer Society Press, Los Alamitos (1995)
Iwahori, Y., Woodham, R.J., Ozaki, M., Tanaka, H., Ishii, N.: Neural Network based Photometric Stereo with a Nearby Rotational Moving Light Source. IEICE Transactions on Information and Systems E80-D(9), 948–957 (1997)
Angelopoulou, E., Wolff, L.B.: Sign of Gaussian Curvature From Curve Orientation in Photometric Space. IEEE Trans. on PAMI 20(10), 1056–1066 (1998)
Okatani, T., Deguchi, K.: Determination of Sign of Gaussian Curvature of Surface from Photometric Data. Trans. of IPSJ 39(5), 1965–1972 (1998)
Iwahori, Y., Fukui, S., Woodham, R.J., Iwata, A.: Classification of Surface Curvature from Shading Images Using Neural Network. IEICE Trans. on Information and Systems E81-D(8), 889–900 (1998)
Iwahori, Y., Fukui, S., Fujitani, C., Woodham, R.J., Iwata, A.: Relative Magnitude of Gaussian Curvature from Shading Images Using Neural Network. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3681, pp. 813–819. Springer, Heidelberg (2005)
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2), 302–309 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iwahori, Y., Nakagawa, T., Fukui, S., Kawanaka, H., Woodham, R.J., Adachi, Y. (2007). Improvement of Accuracy for Gaussian Curvature Using Modification Neural Network. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_126
Download citation
DOI: https://doi.org/10.1007/978-3-540-74827-4_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74826-7
Online ISBN: 978-3-540-74827-4
eBook Packages: Computer ScienceComputer Science (R0)