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Event-based image processing is a relatively new domain in the field of computer vision. Much research has been carried out on adapting event-based data to comply with established techniques from frame-based computer vision. On the contrary, this paper presents a descriptor which is designed specifically for direct use with event-based data and therefore can be considered to be a pure event-based vision descriptor as it only uses events emitted from event-based vision devices
without transforming the data to accommodate frame-based vision techniques. This novel descriptor is known as the Poststimulus Time-dependent Event Descriptor (P-TED). P-TED is comprised of two features extracted from event data which describe motion and the underlying pattern of transmission respectively. Furthermore a framework is presented which leverages the P-TED descriptor to classify motions within event data. This framework is compared against another stateof-the-art event-based vision descriptor as well as an established frame-based approach.
Original languageEnglish
Title of host publicationThe 27th IEEE International Conference on Image Processing (ICIP 2020)
Publication statusAccepted/In press - 16 May 2020
EventThe 27th IEEE International Conference on Image Processing - Abu Dhabi, United Arab Emirates
Duration: 25 Oct 202028 Oct 2020


ConferenceThe 27th IEEE International Conference on Image Processing
Abbreviated titleICIP 2020
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi


  • Bio-inspired
  • neuromorphic
  • Motion Recognition
  • Multi-dimensional Signal Processing
  • Computer Vision


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