Abstract
The problem of recovering shape from shading is important in computer vision and robotics and several studies have been done. We already proposed a versatile method of solving the problem by model inclusive learning of neural networks. The method is versatile in the sense that it can solve the problem in various circumstances. Almost all of the methods of recovering shape from shading proposed so far assume that illumination conditions are known a priori. It is, however, very difficult to identify them exactly. This paper discusses a method to solve the problem. We propose a model inclusive learning of neural networks which makes it possible to recover shape with simultaneously estimating illumination directions. The performance of the proposed method is demonstrated through some experiments.
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Kuroe, Y., Kawakami, H. (2015). Model Inclusive Learning for Shape from Shading with Simultaneously Estimating Illumination Directions. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_55
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DOI: https://doi.org/10.1007/978-3-319-26532-2_55
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