【摘要】 基于图像交叉熵的图像匹配方法对于噪声不敏感,并且具有一定的抗几何失真能力,但算法复杂度高,不适合用于实时匹配系统中.而投影变换可将图像的二维灰度降为一维的特征向量,且还具有抗噪性好的特性,因此定义图像的局部交叉投影熵,提出了一种新的图像匹配算法.该算法首先计算模板图的行、列投影;然后计算模板图和实时图的交叉投影熵;最后根据行、列交叉投影矩阵确定出最优匹配坐标.新算法不仅具有较好的抗噪和抗几何失真性能,并且提高了在强光照射及云层遮挡情况下的匹配能力.通过实验仿真并对比局部熵、局部投影熵、局部交叉熵和局部交叉投影熵四种算法的匹配效果,表明该算法不仅匹配效果良好,并且计算速度快,是一种精确而实用的图像匹配方法.更多还原

【Abstract】 Image cross-entropy matching algorithm has neither noise sensitivity nor rotational variability,but it is not suitable for real-time matching system because of its high complexity.Because the feature dimension of an image can be reduced by projection transformation,cross projection entropy is defined and a novel image matching algorithm is proposed.Firstly,row and column projections of the template image are separately calculated;secondly,the cross projection entropy between the template image and the real-time image is computed;lastly,the optimal matching coordinate is determined according to the cross projection entropy matrix.This image matching algorithm has good anti-noise capability,and good matching results can be got in high light condition and in cloud cover condition.A contrast experiment of four image matching methods of local entropy,local projection entropy,local cross-entropy and local cross projection entropy is conducted.The results show that the proposed algorithm has fairly good matching performance,high running speed.It proves to be a precise and practical image matching method.更多还原