https://github.com/cewdlr/ebiv Event-based vision (EBV), also termed dynamic vision sensing (DVS) or neuromorphic imaging, is a new upcoming field within the field of computer vision. Contrary to conventional frame-based imaging, EBV only records changes of image intensity (i.e., contrast changes) on the pixel level, triggering a positive event (
) for increasing intensity and a negative event (
) for a decreasing intensity change. The typical threshold of the intensity-change trigger is on the order of 20% but can be fine-tuned. As the pixels on the detector respond individually, the events appear asynchronously throughout the detector area resulting in a continuous stream of asynchronous data, with each event datum
consisting of pixel coordinates
, a time stamp
and a polarity
indicating the sign of the intensity change.
Original prototype development of the technology dates back to work of Mahowald and colleagues at the California Institute of Technology in the 1990’s and was initially referred to as silicon retina (Mahowald 1992) as the intention of the imaging approach was to mimic the function of the eye’s retina. First practical implementations of EBV resulted from work at University of Zurich as well as ETH Zurich around 2008 (Lichtsteiner et al. 2008; Posch et al. 2014). In recent years, several ready-to-use cameras and sensor evaluation kits based on the EBV technology have become commercially available. This has broadened the range of applications as testified in a steadily increasing number of publications (see, e.g., Robotics and Perception Group 2022) and also has made the present feasibility study possible. For recent reviews of event-based sensing technology and underlying concepts, the reader is referred to Gallego et al. (2022) and Tayarani-Najaran and Schmuker (2021)
روش TOPSIS جهت یافتن راه حل بهینه در مسائل چند هدفه