Yong‐Xia Wang

Tianjin University of Technology and Education

Papers

Total Citations

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About

Yong‐Xia Wang’s research centers on computer vision and robotics, with a particular focus on real-time object tracking. Her major contributions lie in the development of robust tracking algorithms that integrate Speed-Up Robust Features (SURF) with advanced filtering techniques. In her most-cited work, “Research of tracking robot based on SURF features” (6 citations), she proposed a novel tracking method combining Kalman Filter (KF) with SURF feature matching, using RANSAC for robust correspondence and Fuzzy C-Means (FCM) clustering to eliminate outliers. This work demonstrated how to maintain tracking accuracy even under challenging conditions. Her follow-up study, “Research of Tracking Models Based on SURF” (4 citations), further explored multiple filter-based tracking models for robotic applications, systematically comparing their performance. Though her citation counts are modest, Wang’s research represents foundational work in applying SURF features to autonomous robot navigation and target tracking. Her contributions are particularly notable for their practical approach to solving real-world tracking problems, offering clear methodologies that have informed subsequent research in feature-based robotic vision.