首页> 外文期刊>Optics Communications: A Journal Devoted to the Rapid Publication of Short Contributions in the Field of Optics and Interaction of Light with Matter >Profilometry for the measurement of three-dimensional object shape using radial basis function, and multi-layer perceptron neural networks
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Profilometry for the measurement of three-dimensional object shape using radial basis function, and multi-layer perceptron neural networks

机译:使用径向基函数和多层感知器神经网络的轮廓测量法,用于测量三维物体形状

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摘要

Neural networks have been used to carryout calibration process in fringe projection profilometry for the measurement of three-dimensional object shape. The calibration procedure uses several calibration planes whose positions in space are known. Radial basis function based networks and multi-layer perceptron networks are investigated for the phase recovery. Preliminary studies are also presented for the direct reconstruction of the object without the use of the intermediate step of phase plane calculations. Experimental results are presented for diffuse objects. (C) 2002 Elsevier Science B.V. All rights reserved. [References: 20]
机译:神经网络已被用于在条纹投影轮廓测量法中执行校准过程,以测量三维物体形状。校准过程使用多个校准平面,其空间位置已知。研究了基于径向基函数的网络和多层感知器网络的相位恢复。还提出了不使用相平面计算的中间步骤而直接重建对象的初步研究。给出了弥散物体的实验结果。 (C)2002 Elsevier Science B.V.保留所有权利。 [参考:20]

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