Maria Isabel de la Fuente
Papers
2
Total Citations
8
H-Index
2
About
Maria Isabel de la Fuente is a researcher specializing in embedded systems, computer vision, and robotics for industrial automation. Her work focuses on the hardware implementation of neural-network-based recognition modules, particularly for visual servoing in mobile robots designed to assist workers in manufacturing environments. Her most cited paper, "Hardware Implementation of a Neural-Network Recognition Module for Visual Servoing in a Mobile Robot" (2010, 5 citations), details the early development of an object detection system integrated into a mobile robot using Field Programmable Gate Arrays (FPGAs). This work laid the foundation for real-time, embedded visual processing in industrial scenarios. A related paper, "Development of an embedded system for visual servoing in an industrial scenario" (2010, 3 citations), further explores FPGA-based algorithms for object detection, emphasizing the system's ability to capture and process images from a camera mounted on the robot. De la Fuente’s contributions are significant for advancing cost-effective, low-latency vision systems that enhance human-robot collaboration in factories. Her research bridges the gap between theoretical neural networks and practical, hardware-constrained applications, making her a notable figure in the field of industrial robotics and embedded vision.
Research Focus
Key Achievements
Top Papers
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