A Novel Extraction Algorithm for Pixel Recognition in AI Images with Feature Point

In the era of big data, image processing still faces significant bottlenecks compared to other fields of computer science. In this paper we studied the feature point extraction algorithm based on gray points and the stereo matching technique, both of them play important roles in image processing. The feature point extraction algorithm based on gray points includes Moravec algorithm and Susan Algorithm. The results indicate that it can discover that simplified inverse filter tracking (SIFT) algorithm is the best algorithm among all the feature point extraction algorithms, which has have strong stability, fast calculating speed and high accuracy. In stereo image matching technique, we found that dense matching algorithm plays an important role in target detection and tracking in the three-dimensional reconstruction operations. By comparing the matching process between traditional nearest neighbor matching and this paper's matching algorithm, we concluded that the matching relationship in this algorithm is entirely correct.