A better aerial video image mosaic algorithm using SIFT combined descriptors

Aim: The introduction of the full paper points out that: (1) Ref.1 uses local descriptors; (2) Refs.1 and 2, both by D. G. Lowe, mention the possibility of utilizing the similarity between consecutive frames to obtain global shape information. We believe that we have succeeded in utilizing the above-mentioned similarity to obtain actually global shape information. Section 1 explains the SIFT (scale invariant feature transform) combined descriptor; its core consists of: it firstly calculates the classical features of local SIFT descriptor, receives the global descriptor of each feature point by introducing the shape information of the image, and then obtains combined descriptors. Fig. 3 in section 2 presents the flow chart of aerial video image mosaic algorithm using SIFT combined descriptors. Section 3 explains image matching; its core consists of: for bidirectional matching, Euclidean distance is utilized for local descriptor, Chi-square distance for the global one, and then, the matched distance of combined descriptors is obtained with the weighted average integration method. Section 4 explains the parameter estimation obtained with the RANSAC (random sample consensus) algorithm and the least squares method. The experimental results, presented in Figs. 5, 6 and 7, show preliminarily that our algorithm has better robustness and is indeed better than that of Ref.1.