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A NEURAL NETWORK APPROACH TO SHAPE FROM SHADING 

Authors: Tianzi Jiang a;  Bing Liu a;  Yingli Lu a; David Evans b
Affiliations:   a National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
b Department of Computing and Mathematics, Nottingham Trent University, Nottingham, NG1 4BU, UK.
DOI: 10.1080/0020716021000038983
Publication Frequency: 15 issues per year
Published in: journal International Journal of Computer Mathematics, Volume 80, Issue 4 April 2003 , pages 433 - 439
Number of References: 11
Formats available: PDF (English)
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Abstract

In this paper, we propose a method of recovering shape from shading that solves directly for the surface height using neural networks. The main motivation of this paper is to provide an answer to the open problem proposed by Zhou and Chellappa [11]. We first formulate the shape from shading problem by combining a triangular element surface model with a linearized reflectance map. Then, we use a linear feed-forward network architecture with six layers to compute the surface height with a singular value decomposition. The weights in the model initialized using eigenvectors and eigen-values of the stiffness matrix of objective functional. Experimental results show that our solution is very effective.
Keywords: Shape From Shading; Neural Networks; Triangular Element Surface Model
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