摘要
SURF(speed-up robust features,即加速健壮特征)算法是一种尺度不变、旋转不变且性能较好的算法,但其稳定性和时间复杂度不足,不稳定的特征点被检测出来,会导致多余的计算。为此,提出用信息量扩展SURF检测算子和分特征集匹配方法,提高算法性能和配准速度,即先检测周围Hessian值最大的特征点,再用SURF算法计算特征点的信息量,然后根据尺度分解特征集成亚集,再根据亚集匹配,最后根据RANSAC和最小二乘法配准。实验结果证明,改进算法的配准性能与SURF算法相当,配准速度比SURF算法更快。
SURF is a scale and in-plane rotation invariant detector and descriptor with better performance,but their stabilities and time complexity are not good enough and unstable features are often detected,which results in needless calculation.The method which extends the detector with information theory and divides the features into sub-collection is proposed to improve performance and matching speed of the algorithm.Firstly detects the maximum point of Hessian around,secondly calculates its information by SURF,then divides the features extracted from both the test and the model object image into several sub-collection,finally the mapping relationship between images is acquired using RANSAC and least squares techniques.The experimental results show that the improved algorithm has the same registration performance but faster speed than SURF.
出处
《湖南工业大学学报》
2011年第2期95-99,共5页
Journal of Hunan University of Technology
基金
湖南省自然科学基金资助项目(09JJ3115)
关键词
SURF算法
改进SURF算法
信息量
图像配准
SURF algorithm
improved SURF algorithm
information quantity
image registration
