关键词: 图像拼接, 尺度不变特征变换（SIFT）, 加速稳健特征（SURF）, Harris, K-Means, 一致性校验

Abstract: A new algorithm combines with Harris angular points, SURF algorithm and K-Means algorithm for accurately matching and stitching microscope images which are characterized by a great number of local feature points. It is an improved algorithm based on traditional algorithm. Specific procedures are as follows. Firstly, feature points in a microscope image are extracted by Harris algorithm and SURF descriptors are constructed to make rough matching through the nearest neighbor algorithm. Next, K-Means clusters rough-matched feature points into groups and obtains clustering center. Therefore, feature points in effective clustering groups are got. The last step is to make precise matching to effective feature points. On this basis, slope consistency and distance consistency of matched feature point pairs are verified so that precise feature point pairs are obtained. As the result, the new algorithm is with high robustness and stability, which can decrease image joint errors and shorten image joint time in image matching and stitching with a great number of local feature points. It is convinced that this new algorithm applies to real-time microscope image matching and stitching well.

Key words: image stitching, Scale-Invariant Feature Transform（SIFT）, Speeded Up Robust Features（SURF）, Harris, K-Means, consistency check