Neural Network Architectures for the Direct Perception of 3D Motion and Depth

一种大规模并行的方法来跟踪刚体运动， 图像序列可以基于李代数形式体系。 这 研究将扩展这种神经网络的理论基础 可视化分析，通过连接进行实验验证 机器模拟。 关键在于， 跟踪吉布森的“光学阵列流”中的变化可以 通过局部布线霍夫曼轨道， 正则李分解 这类似于重复 霍夫变换的应用，但是这里将与 一种利用视觉流动的“动量”的新技术。 这一工作的灵感来自皮茨和麦卡洛克的理论 关于从变换群中提取共相。 的 目标是在位置和深度上同时跟踪对象， 而不首先将它们从光场分割。

A massively parallel approach to the tracking of rigid motions in image sequences can be based on a Lie-algebra formalism. This research will extend the theoretical basis of such neural-network visual analysis, with experimental verification via a Connection Machine simulation. The key insight is that instantaneous tracking of variations in Gibson's "flow of the optic array" can be achieved by local wiring of Hoffman orbits derived from a canonical Lie decomposition. This is similar to repeated application of a Hough transform, but will here be combined with a new technique for exploiting "momentum" of the visual flow. Inspiration for this work comes from Pitts and McCulloch's theory on the extraction of universals from transformation groups. The goal is to track objects simultaneously in position and depth, without first segmenting them from the optic field.