基于改进SIFT-Harris的图像配准算法OA

For the existing Harris scale space combined algorithms with high complexity,low accuracy and poor real-time performance,an improved algorithm is proposed.Establish a scale space according to scale-invariant feature transform(SIFT)algorithm to detect Harris feature points,describe features using a 32 dimensional vector.Use vector similarity to match feature points.The Classic K-means algorithm is improved.It has not a fixed initial value,takes the candidate class center point with large distance and low correlation as the initial class center point and categorizes feature points into the class with the smallest distance.Three pairs of matching points were randomly selected from classes of feature points of two images to form a pair of triangles.The matching points are further filtered by triangles similarity.The improved RANSAC algorithm assigns values to all match points based on the absolute values of match point errors to jointly evaluate the transformation model.The experimental results show that the number of feature points extracted by this algorithm is about 22%less than that of SIFT and Harris algorithm,the matching accuracy is improved by about 13%,and the operation time is decreased by about 4.7%.