Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals - Ieng, Sio-Hoi; Lehtonen, Eero; Benosman, Ryad (Frontiers in Neuroscience, 2018)
This paper introduces an event-based methodology of 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 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.