中文摘要：

针对传统图像拼接算法特征点计算量大、耗时较长等问题,提出了一种基于小波变换的新型加速鲁棒特征算法（SURF）图像拼接方法。首先通过Haar小波函数对图像进行二阶分解以获取图像低频成分,并利用小波梯度矢量对低频图像重合区域进行特征点提取,从而实现低频图像下快速获得特征点的变换参数以指导高频图像下的特征点提取;在此基础上,提出一种SURF图像匹配改进算法,利用特征点约束的单向匹配和方向一致等性质,有效剔除误匹配点对,以提高特征点匹配精度和实时性。最后,通过两组实验验证了所提出方法的有效性和可行性。

英文摘要：

In order to solve the problem of large computation and long time consuming of traditional image mosaic algorithm, a novel SURF image mosaic method based on wavelet transform is proposed in this paper.Firstly, adopting the Haar wavelet image preprocessing method to get the second order decomposition, the low frequency components of image are obtained.And by using wavelet gradient vector, the feature point can be extracted for low frequency image overlap region.So, the transformation parameters of characteristic point are quickly acquainted in low frequency images, which can guide to select the feature point extraction in high frequency images.Based on that, an improved SURF image matching algorithm is proposed by using the properties of the single direction matching and the orientation coherence, which can effectively eliminate the mismatched point pair to improve the accuracy and real-time performance of feature point matching.Finally, two experiments are used to verify the feasibility and effectiveness of the proposed results.