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24 April 2024 |
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Material Approximation of Data Smoothing and Spline Curves Inspired by Slime Mould | | Jeff Jones
; Andrew Adamatzky
; | | Date: |
11 Mar 2015 | | Abstract: | Using a particle model of Physarum displaying emer- gent morphological
adaptation behaviour we demonstrate how a minimal approach to collective
material computation may be used to transform and summarise properties of
spatially represented datasets. We find that the virtual material relaxes more
strongly to high-frequency changes in data which can be used for the smoothing
(or filtering) of data by ap- proximating moving average and low-pass filters
in 1D datasets. The relaxation and minimisation properties of the model enable
the spatial computation of B-spline curves (approximating splines) in 2D
datasets. Both clamped and unclamped spline curves, of open and closed shapes,
can be represented and the degree of spline curvature corresponds to the
relaxation time of the material. The material computation of spline curves also
includes novel quasi-mechanical properties including unwind- ing of the shape
between control points and a preferential adhesion to longer, straighter paths.
Interpolating splines could not directly be ap- proximated due to the formation
and evolution of Steiner points at nar- row vertices, but were approximated
after rectilinear pre-processing of the source data. This pre-processing was
further simplified by transform- ing the original data to contain the material
inside the polyline. These exemplar results expand the repertoire of spatially
represented uncon- ventional computing devices by demonstrating a simple,
collective and distributed approach to data and curve smoothing. | | Source: | arXiv, 1503.3264 | | Services: | Forum | Review | PDF | Favorites | |
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