摘要
分别就两种约束使用神经网络对三维刚体运动进行参数估计。一是基于三维点匹配,将预测的运动参数作用于运动前的坐标,与运动后坐标进行比较;二是基于二维运动场,将使用预测的运动参数计算得出的二维运动场与图像序列中计算得出的二维运动场进行比较。两个神经网络均使用Newton-Raphson方法更新权值,以达到目标误差最小化。通过实验验证了该神经网络方法。
Neural networks is used to estimate three-dimensional （3D） rigid motion parameters based two constraints. The one is based on 3D correspondences. The points＇ coordinates before motion are updated by the presupposed motion parameters and then compared to those after motion. The other is based two-dimensional （2D） motion fields. The motion fields from presupposition are compared to those computed from image sequences. Both network updates its weights by newton-raphson procedure for minimizing the error measures. Experimental results are presented for validating the proposed scheme.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第12期3163-3166,共4页
Computer Engineering and Design
基金
国家自然科学基金项目(60473038)
