Fast Stitching Algorithm Based on Perceptual HASH and Scale Invariant Feature Transformation

Abstract:

The k-d tree registration algorithm has the disadvantages of too much computation for high-dimensional features, less use of SIFT feature information and a lot of errors in the registration results, due to the complexity of scale invariant feature transformation (SIFT). Therefore, a fast stitching algorithm based on perceptual hash and scale invariant feature transformation is proposed. Firstly, the perceptual HASH algorithm is used to extract the hash fingerprint of the matching image and that of the image to be matched, and the similar parts of the two images are quickly identified. Then the SIFT feature points of similar areas are calculated and extracted. In the feature point registration algorithm, the traditional k-d tree algorithm is replaced. The main direction and coordinate position information of SIFT feature points are used to filter out the unnecessary feature point matching and reduce the registration time. Finally, the best weighted seam image fusion algorithm is used to eliminate the mutation and complete the stitching. Experimental results show that the number of feature points extracted by this algorithm is less than the number of feature points extracted by the traditional algorithm, and the amount of calculation is reduced in the registration algorithm. At the same time, some mismatches are roughly filtered, which improves the matching accuracy, and the time-consuming of the algorithm is significantly improved compared with the traditional method.