Jia Xue

Rutgers Sexual and Reproductive Health and Rights, Rutgers, The State University of New Jersey

Papers

Total Citations

187

H-Index

About

Jia Xue is a leading researcher in computer vision and robotics, specializing in ground terrain recognition and material perception. Her most impactful work, "Deep Texture Manifold for Ground Terrain Recognition" (2018, 146 citations), introduced the Deep Encoding Pooling Network (DEP), a novel texture network that significantly advanced how robots and autonomous vehicles interpret outdoor environments for control and localization. By addressing the challenge of recognizing diverse ground surfaces under varying conditions, Xue’s DEP architecture provides a robust, data-driven solution that bridges the gap between controlled laboratory measurements and real-world scene variability. Her follow-up study, "Differential Viewpoints for Ground Terrain Material Recognition" (2020, 31 citations), further pushes boundaries by exploring multi-view imaging to enhance material recognition, offering a middle-ground approach that balances radiometric precision with practical, internet-mined data. This work has direct implications for autonomous navigation, field robotics, and environmental monitoring. Xue’s contributions are widely cited for their practical impact, with her DEP network becoming a benchmark in terrain classification. Her research continues to shape how machines perceive and interact with complex outdoor terrains, making her a key figure in advancing robotic autonomy and intelligent vehicle systems.