基于结构照明和BP神经网络的三维物体识别

提出一种具有旋转不变性的三维物体识别的新方法,该方法通过结构光照明的方法,使物体的高度分布以变形条纹的形式编码于二维强度图中,由于条纹图包含有物体的高度分布信息,因此对条纹的相关识别具有本征三维识别的特点.旋转不变性是通过BP神经网络实现的.计算机模拟结果表明,用二维强度像的基频分量做训练样本设计BP神经网络,选择训练样本和隐藏层神经元的数目,基于结构光编码的BP神经网络对三维物体具有良好的旋转不变识别效果.

Rotation-invariant 3-D object recognition based on structured light illumination and BP neural networks

An new method for rotation-invariant three-dimensional(3-D) object recognition was proposed. The method was based on the use of 2-D information encoded in the form of deformed fringe pattern which was obtained when a grating was projected onto an object's surface. The deformed fringe patterns contained the height information about the objects,so the method had the characteristic of intrinsic 3-D recognition. The rotation invariance was achieved by BP neural networks. Through designing BP neural networks with the first-order frequency as training samples and selecting the number of training samples and hiding neurons,the results of computer simulation show that BP neural networks combined with structured light illumination has a better performance for rotation-invariant 3D object recognition.