Papers
8
Total Citations
76
H-Index
4
About
Marina Zhdanova is a researcher specializing in human-robot collaboration, computer vision, and intelligent manufacturing automation. Her work sits at the intersection of machine learning and robotics, focusing on developing intuitive, contactless interfaces that enable seamless interaction between human operators and robotic systems in smart manufacturing environments. Zhdanova's most influential contribution is her development of the 3-D Binary Micro-block Difference method for action recognition, published in 2021 and accumulating 35 citations — a significant achievement that demonstrates the technique's practical relevance to the robotics community. Her earlier foundational work on contactless human action recognition for collaborative robot control systems (2019, 15 citations) and human activity recognition frameworks tailored for human-robot collaboration (2020, 12 citations) helped establish her as a notable voice in the HRC field. More recently, Zhdanova has expanded her research into depth map quality enhancement using deep learning, addressing real-world sensor degradation challenges in industrial settings such as welding and milling. Her work on semantic segmentation for augmented reality further illustrates her commitment to advancing practical, real-time visual intelligence solutions. Collectively, her publications reflect a cohesive research agenda aimed at making robotic systems safer, smarter, and more accessible to human collaborators.
Research Focus
Key Achievements
Top Papers
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- 3Human activity recognition for efficient human-robot collaboration12 citations · 2020
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- 5Deep learning-based depth map defect removal for industrial applications3 citations · 2023
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- 8Real‐time deep learning semantic segmentation for 3-D augmented reality2 citations · 2023