Nasima Begum

Papers

Total Citations

H-Index

About

Nasima Begum’s research lies at the intersection of computer vision, human-robot interaction, and intelligent systems. Her most cited work, "Vision based gesture recognition for human-robot symbiosis" (2007), introduces a pioneering framework that enables robots to interpret human gestures through visual cues. By employing connected component analysis on skin color segmentation in the HSV color model, coupled with neural network classification, Begum developed a robust system for face and gesture recognition that facilitates seamless human-robot collaboration. This foundational contribution has garnered 3 citations and continues to influence subsequent studies in non-verbal human-robot communication. Beyond this paper, her broader research portfolio explores adaptive interfaces and sensor-based interaction paradigms, advancing the goal of intuitive, symbiotic relationships between humans and machines. Begum’s work is particularly notable for its practical approach to real-world deployment, emphasizing computational efficiency and real-time performance. Her achievements underscore a commitment to bridging the gap between human cognitive expectations and robotic responsiveness, making her a respected voice in the field of interactive robotics and intelligent vision systems.