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Shape from Silhouette and Neural Network Based Optimization

In this paper, a new approach is proposed to recover the shape for the restricted observation with the limited rotation angle. This is achieved by combining Shape-from-silhouette and the Hopfield neural network based optimization technique. Under the condition that the number of the observed images is restricted with the limited rotation angle, the original Shape-from-silhouette gives poor result, while the HF-NN optimization gives the high performance with the exact shape through the formulation of the partial derivatives of height and gradient. Further, the approach is quite empirical in that no explicit assumptions are used for the specific surface reflectance function. RBF neural network is used to estimate the image irradiance (i.e. reflectance map R) in the optimization process. Then, computer simulation evaluates the accuracy of our method. Moreover, the experiment by the real object is shown and the effectiveness of the proposed method is demonstrated.