Research on Eliminating Mismatched Feature Points: A Review

The mismatch point elimination algorithm is a commonly used method in the field of computer vision and image processing to deal with the presence of mismatches or outliers in matched point pairs. These mismatch points may be caused by noise, occlusion, illumination changes or image distortion. In this paper, we first explain why there is a need to eliminate the mismatch points and the current state of research, and then introduce various types of feature points and describe the extraction methods of various feature points. Next, we review several methods of false match feature point elimination, such as geometric consistency verification-based methods, graph optimization-based methods, motion statistics-based methods, and learning-based methods, analyze their advantages and disadvantages as well as make comparisons, and give an outlook on future research directions. In the conclusion, we summarize the full paper and discuss the application trends of the mismatching feature point elimination algorithms. The purpose of this paper is to provide readers with a clearer and deeper understanding of false match feature point elimination algorithms, and hopefully give some reference significance to later researchers.