Improved Characters Feature Extraction and Matching Algorithm Based on SIFT

According to SIFT algorithm does not have the property of affine invariance, and the high complexity of time and space, it is difficult to apply to real-time image processing for batch image sequence, so an improved SIFT feature extraction algorithm was proposed in this paper. Firstly, the MSER algorithm detected the maximally stable extremely regions instead of the DOG operator detected extreme point, increasing the stability of the characteristics, and reducing the number of the feature descriptor; Secondly, the circular feature region is divided into eight fan-shaped sub-region instead of 16 square subregion of the traditional SIFT, and using Gaussian function weighted gradient information field to construct the new SIFT features descriptor. Compared with traditional SIFT algorithm, The experimental results showed that the algorithm not only has translational invariance, scale invariance and rotational invariance, but also has affine invariance and faster speed that meet the requirements of real-time image processing applications.