Neuromorphic Computer Vision: Sensing and Neuromorphic Machine Learning for Vision Applications
Not stated
- Funding
- Self-Funded PhD Students Only
- Application deadline
- Year-round applications
About the project
About the Project Recent advances in bio-inspired neuromorphic hardware and neuromorphic sensors enable more efficient methodologies for computer vision tasks, aiming to minimise the cost, latency and energy consumption of real-time vision systems. This project will focus on processing event streams generated from neuromorphic cameras and applying neuromorphic machine learning methods such as Spiking Neural Networks (SNNs) for vision tasks such as object segmentation and recognition, human motion analysis and video understanding. Candidates should have appropriate academic qualifications (first or upper second class honours or MSc degree), in Computer Science, Engineering, Mathematics, Physics or other relevant area, strong background in programming and expertise in Machine Learning / Artificial Intellifence. Qualified applicants are encouraged to contact Prof Dimitrios Makris ( d.makris@kingston.ac.uk ) to informally discuss the project. Supervisor’s profile: https://www.kingston.ac.uk/staff/profile/professor-dimitrios-makris-151/ Google Scholar profile: https://scholar.google.co.uk/citations?user=vHv7JRcAAAAJ