A Lie group approach to a neural system for three-dimensional interpretation of visual motion
A Lie group approach to a neural system for three-dimensional interpretation of visual motion
复制标题
用于视觉运动三维解释的神经系统的李群方法
DOI：
10.1109/72.80302
复制
发表时间：
1991
影响因子：
--
通讯作者：
["Tien
中科院分区：
文献类型：
--
作者：
["Tien
提出了一种新的方法，神经网络计算的三维刚体运动从噪声的二维图像流。它表明，图像流的三维解释过程可以看作是一个线性信号变换。这种线性变换的基本信号是3-D欧几里得群的6个无穷小生成元的2-D向量场。这种转换可以通过神经网络来执行。结果还报告了神经网络模拟的3-D解释的图像流和比较的性能，这种方法与使用传统的方法。计算机仿真结果验证了基于李群的神经网络三维运动感知方法的有效性。
A novel approach is presented to neural network computation of three-dimensional rigid motion from noisy two-dimensional image flow. It is shown that the process of 3-D interpretation of image flow can be viewed as a linear signal transform. The elementary signals of this linear transform are the 2-D vector fields of the six infinitesimal generators of the 3-D Euclidean group. This transform can be performed by a neural network. Results are also reported of neural network simulations for the 3-D interpretation of image flow and a comparison of the performance of this approach with that using conventional methods. Computer simulation results verify the Lie-group-based neural network approach to three-dimensional motion perception.