遗传算法和BP算法相结合进行图像匹配

Using genetic and BP algorithms for image matching

将神经网络理论应用于图像匹配 ,提出采用遗传学习算法进行全局寻优、利用BP算法进行精确训练、优化BP(backpropagation)神经网络权重学习和训练的神经网络图像匹配算法 .实验表明该算法的收敛性能及学习速度优于传统的BP神经网络图像匹配算法及其他同类改进算法 ,具有寻优的全局性和精确性 .

The neural network theory is used for image matching; and a neural network image-matching algorithm is proposed based on genetic al gorithm: first optimizing the BP networks learning process by genetic algorithm ,then accurately training it using the BP algorithm. Finally, according to the obtained weights and thresholds, the matching result are obtained. Experimental results show that this new image-matching algorithm has merits in accuracy, sp eed, and robustness; and its convergence performance is also better than that of standard BP algorithm.