Hopfield neural network image matching based on directional wavelet transform

The image matching algorithm is the core of image matching aided navigation systems. Many algorithms have been proposed to improve the accuracy and real-time performance of image matching. Due to its capability of high-speed information processing and uncertainty information processing, Feature point based Artificial Neural Network image matching method has attracted considerable attention in recent years. Yet the result of Feature point based Artificial Neural Network image matching is affected greatly by many factors, such as object occlusions, lighting conditions and noises, especially the ANN algorithm being used, therefore it is important to find a robust feature point based ANN matching algorithm. This paper presents an image registration method based on a two-dimensional Hopfield Neural Network, where the problem of image matching is treated with the minimization of the energy function of the Hopfield neural network. The input data used for registration are the locations of the points extracted from the images with the Directional Wavelet Transform method. Experiments show that efficiency of the novel algorithm is higher than existed algorithm. Its results are very satisfactory.