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Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals.

Sio-Hoi Ieng1, Eero Lehtonen2, Ryad Benosman1

  • 1INSERM 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.

Frontiers in Neuroscience
|June 28, 2018
PubMed
Summary
This summary is machine-generated.

This study presents an event-based method for signal transformations like the Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT), reducing computational costs for high-temporal-resolution video analysis.

Keywords:
AERDCTDWTdiscrete basis transformsevent-based signal processing

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Area of Science:

  • Signal Processing
  • Computer Vision
  • Neuromorphic Engineering

Background:

  • Traditional signal transformations (e.g., DFT, DWT) are computationally intensive.
  • Frame-based video processing struggles with high temporal resolution requirements.
  • Neuromorphic sensors offer event-based data streams.

Purpose of the Study:

  • Introduce an event-based methodology for arbitrary linear basis transformations.
  • Analyze the computational complexity of the proposed event-based method.
  • Demonstrate the efficiency gains over frame-based methods for specific applications.

Main Methods:

  • Developed an event-based framework for linear basis transformations.
  • Performed complexity analysis comparing event-based and frame-based approaches.
  • Utilized natural video sequences from an asynchronous time-based neuromorphic image sensor (ATIS).

Main Results:

  • The event-based methodology efficiently performs signal transforms like DFT and DWT.
  • Reduced multiply-and-accumulate operations compared to frame-based methods at high temporal resolutions.
  • Experimental validation on ATIS data confirms feasibility and computational resource savings.

Conclusions:

  • Event-based signal processing offers significant computational advantages for high-temporal-resolution data.
  • The proposed methodology provides an efficient alternative for real-time signal analysis in neuromorphic systems.
  • This approach is particularly beneficial for applications requiring detailed temporal information from dynamic scenes.