Abstract
Simulated Annealing (SA) is a powerful stochastic search method that can produce very high quality solutions for hard combinatorial optimization problem. In this paper, we applied this SA method to optimize our 3D hierarchical reconstruction neural network (NN). This NN deals with complicated task to reconstruct a complete representation of a given object relying only on a limited number of views and erroneous depth maps of shaded images. The depth maps are obtained by Tsai-Shah shape-from-shading (SFS) algorithm. The experimental results show that the SA optimization enable our reconstruction system to escape from a local minima. Hence, it gives more exact and stable results with small additional computation time.
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Fanany, M.I., Kumazawa, I. (2003). SA-Optimized Multiple View Smooth Polyhedron Representation NN. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_27
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DOI: https://doi.org/10.1007/978-3-540-39644-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20293-6
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