Leisheng Chen

Nanjing University of Aeronautics and Astronautics

Papers

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About

Leisheng Chen is a researcher focused on advancing computer vision and image processing, particularly in challenging real-world environments. His work centers on developing robust feature detection algorithms that perform reliably under complex illumination conditions, a critical challenge for autonomous systems and robotics. Chen’s most notable contribution, the "Adaptive ORB feature detection with a variable extraction radius in RoI for complex illumination scenes" (2022), has already garnered 14 citations, reflecting its immediate relevance to the field. This paper introduces a novel method that dynamically adjusts the extraction radius of ORB (Oriented FAST and Rotated BRIEF) features within a region of interest, significantly improving feature matching accuracy in scenes with uneven lighting or shadows. By addressing a fundamental limitation of traditional feature detectors, Chen’s work enables more robust visual odometry, SLAM, and object recognition systems. His research is particularly valuable for applications in autonomous navigation, augmented reality, and surveillance, where reliable performance under varying light is essential. With a growing citation impact, Leisheng Chen is establishing himself as a promising contributor to practical, illumination-adaptive computer vision solutions.