• DOI:10.1109/JPROC.2014.2346153
• Corpus ID: 11513955

Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output

@article{Posch2014RetinomorphicEV,
  title={Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output},
  author={Christoph Posch and Teresa Serrano-Gotarredona and Bernab{\'e} Linares-Barranco and Tobi Delbr{\"u}ck},
  journal={Proceedings of the IEEE},
  year={2014},
  volume={102},
  pages={1470-1484}
}

• C. Posch, T. Serrano-Gotarredona, +1 author T. Delbrück
• Published 28 August 2014
• Computer Science
• Proceedings of the IEEE

State-of-the-art image sensors suffer from significant limitations imposed by their very principle of operation. These sensors acquire the visual information as a series of “snapshot” images, recorded at discrete points in time. Visual information gets time quantized at a predetermined frame rate which has no relation to the dynamics present in the scene. Furthermore, each recorded frame conveys the information from all pixels, regardless of whether this information, or a part of it, has changed since the last frame had been acquired. This acquisition method limits the temporal resolution, potentially missing important information, and leads to redundancy in the recorded image data, unnecessarily inflating data rate and volume. Biology is leading the way to a more efficient style of image acquisition. Biological vision systems are driven by events happening within the scene in view, and not, like image sensors, by artificially created timing and control signals. Translating the frameless paradigm of biological vision to artificial imaging systems implies that control over the acquisition of visual information is no longer being imposed externally to an array of pixels but the decision making is transferred to the single pixel that handles its own information individually. In this paper, recent developments in bioinspired, neuromorphic optical sensing and artificial vision are presented and discussed. It is suggested that bioinspired vision systems have the potential to outperform conventional, frame-based vision systems in many application fields and to establish new benchmarks in terms of redundancy suppression and data compression, dynamic range, temporal resolution, and power efficiency. Demanding vision tasks such as real-time 3-D mapping, complex multiobject tracking, or fast visual feedback loops for sensory-motor action, tasks that often pose severe, sometimes insurmountable, challenges to conventional artificial vision systems, are in reach using bioinspired vision sensing and processing techniques.