Eric Wilkinson
Papers
1
Total Citations
5
H-Index
1
About
Eric Wilkinson is a researcher at the intersection of computer vision and robotics, with a primary focus on efficient object recognition in resource-constrained, dynamic environments. His most cited work, "Efficient aspect object models using pre-trained convolutional neural networks" (2015, 5 citations), tackles a critical challenge in robotics: recognizing objects when large training datasets are unavailable and new models must be added on the fly. By leveraging pre-trained CNNs, Wilkinson developed a method that balances accuracy with computational efficiency, enabling robots to adapt to task-specific objects without extensive retraining. This contribution is particularly valuable for applications in autonomous manipulation and service robotics, where flexibility and speed are paramount. While his citation count reflects a focused, early-stage impact, his work addresses a fundamental bottleneck in deploying vision systems on physical platforms. Wilkinson’s research underscores the importance of bridging deep learning with real-world robotic constraints, offering a practical pathway for integrating perception into adaptive, task-driven systems.
Research Focus
Key Achievements
Top Papers
- 1