利用神经网络进行表面重建

Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 1992-06-15 DOI:10.1109/CVPR.1992.223251

D. S. Chen, R. Jain, B. G. Schunck

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引用次数: 14

摘要

提出了一种基于多层前馈神经网络的曲面重构方法。以多层神经网络为代表的参数形式能够以一种比许多经典方法更通用、更灵活的方式对光滑表面分段建模。该近似方法基于鲁棒反向传播(BP)算法，扩展了基本BP算法来处理训练数据中的错误，特别是其他错误。

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Surface reconstruction using neural networks

A surface reconstruction method using multilayer feedforward neural networks is proposed. The parametric form represented by multilayer neural networks can model piecewise smooth surfaces in a way that is more general and flexible than many of the classical methods. The approximation method is based on a robust backpropagation (BP) algorithm, which extends the basic BP algorithm to handle errors, especially others, in the training data.<>

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Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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