S.M. Yamany (Egypt), M.G. Mostafa, and A.A. Farag (USA)
3D reconstruction, Shape from Shading, Dentistry, ImageProcessing, Computer Vision
This paper presents a framework for integrating multiple sensory data, sparse range data and dense depth maps from shape from shading in order to improve the 3D reconstruction of teeth surfaces using intra-oral images. The integration process is based on propagating the error difference between the two data sets by fitting a surface to that difference and using it to correct the visible surface obtained from shape from shading. A feedforward neural network is used to fit a surface to the sparse data. We also study the use of the extended Kalman filter for supervised learning and compare it with the backpropagation algo rithm. An accuracy analysis is done to obtain the best neural network architecture and learning algorithm.
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