E. L. S. Gouveia
Papers
2
Total Citations
4
H-Index
2
About
E. L. S. Gouveia is a researcher working at the intersection of neuromorphic vision and deep learning, with a focus on developing efficient, biologically inspired systems for object classification and tracking. Their work explores how neuromorphic cameras—which mimic the human retina’s event-driven sensing—can be paired with convolutional neural networks to achieve low-latency, high-accuracy visual recognition. Gouveia’s 2022 paper on “Classification of Objects Using Neuromorphic Camera and Convolutional Neural Networks” demonstrates a novel approach to leveraging spiking data for real-time classification, while their companion study on “An Object Tracking Using a Neuromorphic System Based on Standard RGB Cameras” bridges the gap between conventional and neuromorphic hardware, enabling robust tracking without specialized sensors. Though early in their career, with each paper garnering 2 citations, Gouveia’s contributions are notable for their practical integration of neuromorphic principles into standard computer vision pipelines—a step toward energy-efficient, event-driven AI. Their work holds promise for applications in autonomous systems, robotics, and edge computing, where speed and power efficiency are critical.
Research Focus
Key Achievements
Top Papers
- 1
- 2