Abstract
The problem of recovering shape from shading is important in computer vision and robotics. It is essentially an ill-posed problem and several studies have been done. In this paper, we present a versatile method of solving the problem by neural networks. The proposed method introduces the concept of the model inclusive learning with simultaneous estimation of unknown parameters. In the method a mathematical model, which we call ‘image-formation model’, expressing the process that the image is formed from an object surface, is introduced and is included in the learning loop of a neural network. The neural network is trained so as to recover the shape with simultaneously estimating unknown parameters in the image-formation model. The performance of the proposed method is demonstrated through experiments.
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References
Horn, B.K.P., Brooks, M.J. (eds.): Shape from Shading. The MIT Press, Cambridge (1989)
Klette, R., et al.: Computer Vision: Three-Dimensional Data From Images. Springer, Heidelberg (1998). pp. 263–345
Wei, G.Q., Hirzinger, G.: Learning shape from shading by a multilayer network. IEEE Trans. Neural Netw. 7(4), 985–995 (1996)
Kuroe, Y., Kawakami, H.: Versatile neural network method for recovering shape from shading by model inclusive learning. In: Proceedings of International Joint Conference on Neural Networks, pp. 3194–3199 (2011)
Kuroe, Y., Kawakami, H.: Estimation method of motion fields from images by model inclusive learning of neural networks. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5769, pp. 673–683. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04277-5_68
Kuroe, Y., Kawakami, H.: Shape from shading by model inclusive learning with simultaneously estimating reflection parameters. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 443–450. Springer, Cham (2014). doi:10.1007/978-3-319-11179-7_56
Kuroe, Y., Kawakami, H.: Model inclusive learning for shape from shading with simultaneously estimating illumination directions. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9489, pp. 501–511. Springer, Cham (2015). doi:10.1007/978-3-319-26532-2_55
Kuroe, Y., Nakai, Y., Mori, T.: A learning method of nonlinear mappings by neural networks with considering their derivatives. In: Proceedings of the IJCNN, Nagoya, Japan, pp. 528–531 (1993)
Torrance, K.E., Sparrow, E.M.: Theory for off-specular reflection from roughened surfaces. J. Opt. Soc. Am. 57(9), 1105–1114 (1967)
Luenberger, D.G.: Introduction to Linear and Nonlinear Programming. Addison- Wesley, Boston (1973). pp. 194–197
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Kuroe, Y., Kawakami, H. (2017). Shape from Shading by Model Inclusive Learning Method with Simultaneous Estimation of Parameters. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_20
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DOI: https://doi.org/10.1007/978-3-319-68612-7_20
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