Artificial neural networks for 3-D motion analysis. I. Rigid motion

Proposes an approach applying artificial neural net techniques to 3D rigid motion analysis based on sequential multiple time frames. The approach consists of two phases: (1) matching between every two consecutive frames and (2) estimating motion parameters based on the correspondences established. Phase 1 specifies the matching constraints to ensure a stable and coherent feature correspondence establishment between two sequential time frames and configures a 2D Hopfield neural net to enforce these constraints. Phase 2 constructs a 3-layer net to estimate parameters through supervised learning. The method performs motion analysis based on sequential multiple time frames. It represents an effective way to achieve optimal matching between two frames using neural net techniques. The energy function of the Hopfield net is designed to reflect the matching constraints and the minimization of this function leads to the optimal feature correspondence establishment. The approach introduces the learning concept to motion estimation. The structure of the net provides the flexibility in estimating motion parameters based on information from multiple frames