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Event-Based Color Segmentation With a High Dynamic Range Sensor.

Alexandre Marcireau1, Sio-Hoi Ieng1, Camille Simon-Chane1

  • 1Institut National de la Santé et de la Recherche Médicale, UMRI S 968, Sorbonne Universites, UPMC Univ Paris 06, UMR S 968, Centre National de la Recherche Scientifique, UMR 7210, Institut de la Vision, Paris, France.

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Summary
This summary is machine-generated.

This study presents a novel color event-based camera for high-speed visual processing. The system achieves efficient color segmentation and tracking using a unique asynchronous sensor design.

Keywords:
AERcolor segmentationevent-based signal processingsilicon retinatracking

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Area of Science:

  • Neuromorphic Engineering
  • Computer Vision
  • Sensor Technology

Background:

  • Traditional cameras have limitations in temporal resolution and dynamic range.
  • Event-based cameras offer high temporal resolution and low latency.
  • Color information is crucial for many computer vision tasks but challenging for event-based sensors.

Purpose of the Study:

  • To introduce a color asynchronous neuromorphic event-based camera.
  • To develop a methodology for processing color output from the sensor.
  • To perform color segmentation and tracking at the sensor's native temporal resolution.

Main Methods:

  • A color vision sensor prototype combining three Asynchronous Time-based Image Sensors (ATIS) was developed.
  • A computationally inexpensive color processing algorithm was devised to leverage absolute color information from the ATIS.
  • The system's performance was evaluated using both controlled indoor and uncontrolled outdoor scenes.

Main Results:

  • The developed system enables color segmentation and tracking at microsecond temporal resolution.
  • The color processing algorithm is computationally efficient, benefiting from asynchronous acquisition.
  • Outdoor tracking achieved a mean error ranging from two to twenty pixels compared to ground truth.

Conclusions:

  • The novel color event-based camera and processing methodology significantly advance real-time color vision capabilities.
  • Low-level processing benefits substantially from asynchronous data acquisition and high temporal resolution.
  • The system demonstrates potential for applications requiring high-speed, accurate color-based object detection and tracking.