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.
CITATION STYLE
Kuroe, Y., & Kawakami, H. (2015). Model inclusive learning for shape from shading with simultaneously estimating illumination directions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9489, pp. 501–511). Springer Verlag. https://doi.org/10.1007/978-3-319-26532-2_55
Mendeley helps you to discover research relevant for your work.