UGC: Real-Time, Ultra-Robust Feature Correspondence via Unilateral Grid-Based Clustering
Zhaohui Zheng, Yong Ma, Hong Yuan Zheng, Jianping Ju, Mingyu Lin · IEEE Access · 2018
Quickly establishing reliable correspondence between two feature sets is a challenging task for feature matching. However, the key to successful feature matching is not only matching robustness but also the precision and real-time performance. It is difficult to achieve both efficiency and efficacy using the current algorithms. In this paper, we propose unilateral grid-based clustering (UGC), which creates a unilateral grid of an image's features and meanshift clustering constraints of the other image correspondence features. UGC removes a large number of mismatches using clustering center statistical analysis of the match feature points in a grid region. For low texture, blur and wide-baselines feature matching of images, UGC provides a realtime, ultra-robust correspondence system. Extensive experiments on image data sets demonstrate the higher precision and real-time performance of UGC, which outperforms current state-of-the-art methods, including conditions such as low contrast and high exposure.