A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time

TL;DR

This paper introduces a novel time-causal, time-recursive scale-space framework for real-time temporal signal smoothing that guarantees scale covariance and simplifies multi-scale analysis without future data access.

Contribution

It develops a canonical time-causal limit kernel ensuring scale covariance and recursive smoothing, suitable for real-time temporal signal processing and multi-scale modeling.

Findings

Proposes a time-causal, recursive scale-space representation.

Establishes a canonical temporal kernel with scale covariance.

Applicable for real-time continuous and digital temporal signal processing.

Abstract

This article presents an overview of a theory for performing temporal smoothing on temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales are guaranteed to constitute simplifications of corresponding temporally smoothed signals at any finer temporal scale (including the original signal) and (ii) the temporal smoothing process is both time-causal and time-recursive, in the sense that it does not require access to future information and can be performed with no other temporal memory buffer of the past than the resulting smoothed temporal scale-space representations themselves. For specific subsets of parameter settings for the classes of linear and shift-invariant temporal smoothing operators that obey this property, it is shown how temporal scale covariance can be additionally obtained, guaranteeing that if the temporal input signal is rescaled by a uniform scaling factor, then also the resulting temporal scale-space representations of the rescaled temporal signal will constitute mere rescalings of the temporal scale-space representations of the original input signal, complemented by a shift along the temporal scale dimension. The resulting time-causal limit kernel that obeys this property constitutes a canonical temporal kernel for processing temporal signal in real-time scenarios when the regular Gaussian kernel cannot be used because of its non-causal access to information from the future and we cannot additionally require the temporal smoothing process to comprise a complementary memory of the past beyond the information contained in the temporal smoothing process itself, which in this way also serves as a multi-scale temporal memory of the past. This theory is generally applicable for both: (i) modelling continuous temporal phenomena over multiple temporal scales and (ii) digital processing of measured temporal signals in real time.