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景象匹配是计算机视觉研究的一个重要方向,如何在复杂的环境下提高匹配概率与定位精度是其亟待解决的难点之一.在分析畸变校正的量化噪声为高斯噪声的基础上,提出了基于特征和时空关联的积相关图像匹配算法.采用基于噪声抑制小波边缘检测方法,提取实时图和基准图的边缘特征;基于归一化积相关实现序列实时图(3帧)与基准图的匹配,再根据位置信息实现相关峰值的数据融合得到匹配点.此方法克服了因面积增大由几何失真导致的匹配概率下降的缺点,提高了匹配概率,具有较好的匹配稳健性.“,”Image registration is an important subject in computer vision. How to improve the probability of registration and precision of localization is a hard task. A product correlation algorithm of image registration based on feature and spatial-temporal relationship is put forward. Firstly, extracting edge features of image using wavelet for sequential real-time images and base image. Secondly, the sequential real-time images (3 frame) are matched with base image using normalized cross-correlation, and the registration points searched in sequential correlation matrix are fused according to the offsets. This method overcomes the shortcoming that the probability of registration is decreased with area increasing because of geometric distortion, and improves the robustness of registration.