Rebecca Fay

Papers

Total Citations

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About

Rebecca Fay’s research lies at the intersection of robotics, computer vision, and neural computation, with a focus on developing neurobotic systems that integrate visual attention, object recognition, and associative information processing. Her most cited work, “Combining Visual Attention, Object Recognition and Associative Information Processing in a NeuroBotic System” (2005, 26 citations), demonstrates her pioneering approach to creating biologically inspired architectures for autonomous robots. In her earlier paper, “Learning Object Recognition in a NeuroBotic System” (2004, 17 citations), Fay addressed the critical challenge of object localisation and identification for mobile service robots. She developed a system that employs a colour-based visual attention control algorithm alongside a hierarchical neural network for object classification, enabling robots to learn and recognise objects in dynamic environments. Though her citation counts are modest, Fay’s contributions are notable for their early integration of attention mechanisms with neural learning—a precursor to modern deep learning approaches in robotics. Her work exemplifies how neurobotic principles can bridge perception and action, offering foundational insights for researchers exploring embodied cognition and autonomous systems.