The field of neuromorphic vision systems aims to replicate the functionality of biological visual systems by mimicking their physical structure and electrical behaviour. Unlike traditional full-frame sensors, neuromorphic systems process data asynchronously and at the pixel level, modelling biological signalling processes. This allows for high-speed operation with lower energy consumption, making them suitable for applications like autonomous vehicles and embedded robotics. This work introduces the Neuromorphic Event Alarm Time-Series Suppression (NEATS) framework, designed to filter noise and detect outlier behaviours in event data without the need for 2-D transformations. NEATS employs rolling statistics and advanced neuromorphic data structures to minimise noise while identifying changes in scene dynamics. This framework injects attention into scene processing, similar to summarisation frameworks in traditional image processing. A novel event-vision alarm change collection (EACC) database is presented, containing controlled stimuli pattern changes captured using leading neuromorphic imaging devices. This database facilitates future benchmarking of neuromorphic attention frameworks, advancing the development of efficient and accurate artificial vision systems.
|Publication status||Accepted/In press - 15 Sept 2023|
|Event||2023 IEEE Symposium Series on Computational Intelligence: SSCI 2023 - heraton Mexico City Maria Isabel Hotel, Mexico City, Mexico|
Duration: 5 Dec 2023 → 8 Dec 2023
|Conference||2023 IEEE Symposium Series on Computational Intelligence|
|Abbreviated title||SSCI 2023|
|Period||5/12/23 → 8/12/23|