SURF算法在小尺寸图像拼接中参数配置的优化

Parameter setting optimization in SURF algorithm based on small scale image

在图像拼接领域,SURF算法因其出众的时效性和鲁棒性,有着十分广泛的应用。针对SURF算法中特征点提取和描述过程中参数固定,对侧重点不同的图像拼接应用存在变通性较差的问题,提出了从窗口滤波器权值,特征点周围子区域的选择以及子区域内Haar小波变换的采样点范围三方面进行参数配置优化。针对目前主流的流媒体尺寸图像,利用控制变量法在不同的SURF参数配置下,对算法的时效性、准确性和鲁棒性等性能进行了分析;通过特征点匹配率和特征点匹配效率的比较,给出了SURF算法参数的选择策略。仿真结果表明该策略可以有效提高SURF算法在图像拼接中的运算速度和准确性,丰富算法在实时领域的应用。

In the field of image stitching, SURF algorithm is widely used for its excellent real-time procedure and robust. Due to the fixed parameter settings in interest point detection and description, the adjustability of SURF algorithm to different applying situation is relatively weak. In order to improve the range of SURF application, this paper looks into three aspects in the algorithm：the weight of box filter, the sub-region selection and the Haar wavelet sampling point range within the sub-region. In comparing the real-time quality, the stitching accuracy and the robust quality of the algorithm, the experiments using variable control method propose a parameter setting strategy based on small scale image which is widely used in nowadays stream media. In analyzing the interest points match rate and efficiency, the strategies proposed in characterizing the algorithm can extend the use of SURF in real-time field.