Papers
4
Total Citations
116
H-Index
3
About
Liming Huang is a pioneering researcher at the intersection of robotic visual perception and flexible electronics, whose work bridges hardware innovation with intelligent sensing systems. His most impactful contribution is the creation of the first visible-depth-thermal image dataset for salient object detection (SOD), a foundational resource that has garnered 100 citations and directly advances robotic grasping by enabling rapid, accurate object localization in complex environments. Huang’s hierarchical two-stage modal fusion framework further refines triple-modality SOD, achieving state-of-the-art performance for multi-sensor robotic systems. Beyond perception algorithms, he demonstrated remarkable versatility by engineering high-performance organic single-crystal nanowire array transistors on an everyday Sellotape substrate—a breakthrough for low-cost, wearable electronics and robotic sensory skin. His earlier work on dictionary learning for topical object discovery in image collections laid the groundwork for robots to semantically understand unstructured scenes. With a publication record spanning top venues in robotics, materials science, and computer vision, Huang’s research exemplifies how fundamental advances in both hardware substrates and algorithmic fusion can synergistically empower next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 4Discovery of topical object in image collections2 citations · 2015