Abstract
The classical SIFT, SURF and other algorithms based on linear scale decomposition are robust, but they lose local precision, which is easy to cause boundary blur and detail loss, and the real-time performance is generally not good. To overcome the above problems, and considering that ORB is not sensitive to the scale transformation of feature points, an improved algorithm is proposed. After the image is preprocessed, the feature points are first proposed by ORB algorithm, Then use the FREAK algorithm to obtain the binary code descriptor of the feature point for matching, and then adopts KNN algorithm to rough match these points. Then it uses the Random Sample Consensus Algorithm to remove the fault matching and to realize fine match. Finally, it uses the improved weighted average method for image fusion and stitching. Experimental results show that the proposed algorithm is effective for image stitching, moreover the stitching quality and stitching speed are much better than other image stitching algorithms.
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