A1 Refereed original research article in a scientific journal
Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals
Authors: Ieng SH, Lehtonen E, Benosman R
Publisher: FRONTIERS MEDIA SA
Publication year: 2018
Journal: Frontiers in Neuroscience
Journal name in source: FRONTIERS IN NEUROSCIENCE
Journal acronym: FRONT NEUROSCI-SWITZ
Article number: ARTN 373
Volume: 12
Number of pages: 13
ISSN: 1662-453X
DOI: https://doi.org/10.3389/fnins.2018.00373
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/32123815
Abstract
This paper introduces an event-based methodology to perform arbitrary linear basis transformations that encompass a broad range of practically important signal transforms, such as the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT). We present a complexity analysis of the proposed method, and show that the amount of required multiply-and-accumulate operations is reduced in comparison to frame-based method in natural video sequences, when the required temporal resolution is high enough. Experimental results on natural video sequences acquired by the asynchronous time-based neuromorphic image sensor (ATIS) are provided to support the feasibility of the method, and to illustrate the gain in computation resources.
This paper introduces an event-based methodology to perform arbitrary linear basis transformations that encompass a broad range of practically important signal transforms, such as the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT). We present a complexity analysis of the proposed method, and show that the amount of required multiply-and-accumulate operations is reduced in comparison to frame-based method in natural video sequences, when the required temporal resolution is high enough. Experimental results on natural video sequences acquired by the asynchronous time-based neuromorphic image sensor (ATIS) are provided to support the feasibility of the method, and to illustrate the gain in computation resources.
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