Abstract. Seeing enables us to recognise people and things, detect motion, perceive our 3D environment and more. Light stimulates our eyes, sending electrical impulses to the brain where we form an image and extract useful information. Computer vision aims to endow computers with the ability to interpret and understand visual information - an artificial analogue to human vision. Traditionally, images from a conventional camera are processed by algorithms designed to extract information. Event cameras are bio-inspired sensors that offer improvements over conventional cameras. They (i) are fast, (ii) can see dark and bright at the same time, (iii) have less motion-blur, (iv) use less energy and (v) transmit data efficiently. However, it is difficult for humans and computers alike to make sense of the raw output of event cameras, called events, because events look nothing like conventional images. This thesis presents novel techniques for extracting information from events via: (i) reconstructing images from events then processing the images using conventional computer vision and (ii) processing events directly to obtain desired information. To advance both fronts, a key goal is to develop a sophisticated understanding of event camera output including its noise properties. Chapters 3 and 4 present fast algorithms that process each event upon arrival to continuously reconstruct the latest image and extract information. Chapters 5 and 6 apply machine learning to event cameras, letting the computer learn from a large amount of data how to process event data to reconstruct video and estimate motion. I hope the algorithms presented in this thesis will take us one step closer to building intelligent systems that can see with event cameras.
- C. Scheerlinck, “How to See with an Event Camera”, Ph.D. Thesis, Australian National University, Canberra, Australia, 2020.