SURF applied in panorama image stitching

SURF (Speeded Up Robust Features) is one of the famous feature-detection algorithms. This paper proposes a panorama image stitching system which combines an image matching algorithm; modified SURF and an image blending algorithm; multi-band blending. The process is divided in the following steps: first, get feature descriptor of the image using modified SURF; secondly, find matching pairs, check the neighbors by K-NN (K-nearest neighbor), and remove the mismatch couples by RANSAC(Random Sample Consensus); then, adjust the images by bundle adjustment and estimate the accurate homography matrix; lastly, blend images by multi-band blending. Also, comparison of SIFT (Scale Invariant Feature Transform) and modified SURF are also shown as a base of selection of image matching algorithm. According to the experiments, the present system can make the stitching seam invisible and get a perfect panorama for large image data and it is faster than previous method.