Image registration algorithm based on EPF and maximum cross-correlation for target recognition of manipulator
基于EPF最大互相关配准的机械臂目标识别方法

Aiming at the multi-object issue of manipulator self-recognition in complex background, this paper proposed a ima-ge matching algorithm based on edge potential function and maximum cross-correlation. The algorithm integrated EPF theory into calculating the potential function of target contour points that could use the synergistic effect of the single edge points in complex structure efficiently, and established corresponding control points using maximum cross-correlation as similarity mea-surement under the constraints of feature points transformation model, then finished target registering and localization. Simulation and experiment result shows that it has good recognition effect for complex background image with noise and rotation caused by imaging conditions, and accuracy is better than traditional correlation match.