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Title: Scattering Statistics of Generalized Spatial Poisson Point Processes
We present a machine learning model for the analysis of randomly generated discrete signals, modeled as the points of an inhomogeneous, compound Poisson point process. Like the wavelet scattering transform introduced by Mallat, our construction is naturally invariant to translations and reflections, but it decouples the roles of scale and frequency, replacing wavelets with Gabor-type measurements. We show that, with suitable nonlinearities, our measurements distinguish Poisson point processes from common self-similar processes, and separate different types of Poisson point processes.  more » « less
Award ID(s):
1845856
NSF-PAR ID:
10336640
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Page Range / eLocation ID:
5528 - 5532
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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