Lirong Yang

Jiangxi University of Science and Technology

Papers

Total Citations

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About

Lirong Yang is a researcher whose work lies at the intersection of computer vision and geological imaging, with a particular focus on advancing stereo matching techniques for mineral analysis. Their most notable contribution is the development of an improved BT-Census stereo matching algorithm, specifically designed for mineral images. This work, published in 2024, addresses the critical challenge of accurately reconstructing three-dimensional structures from two-dimensional mineral samples—a task essential for resource exploration and geological modeling. By enhancing the traditional Census transform with better noise resilience and edge preservation, Yang’s algorithm achieves higher precision in depth estimation for complex mineral surfaces. Though early in its citation trajectory, with 5 citations to date, the paper has already attracted attention from researchers in both computer vision and geoscience, signaling its potential to bridge these fields. Yang’s work is particularly valuable for automating mineral identification and quantification, reducing the need for manual inspection in mining operations. Their research exemplifies how adapting state-of-the-art computer vision methods to domain-specific problems can yield practical, high-impact tools for natural resource industries.