Spatiotemporal data mining with cellular automata

Authors:
Karl Fu;Yang Cai
Affiliations:
Visual Intelligence Studios, Cylab, CIC-2218, Carnegie Mellon University, Pittsburgh, PA;Visual Intelligence Studios, Cylab, CIC-2218, Carnegie Mellon University, Pittsburgh, PA
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Year:
2006

Citing 4
Cited 0

Cellular automata machines: a new environment for modeling

Cellular automata machines: a new environment for modeling
Controlled generation of two-dimensional patterns based on stochastic cellular automata

Future Generation Computer Systems - Cellular automata CA 2000 and ACRI 2000
Theory of Self-Reproducing Automata

Theory of Self-Reproducing Automata
Visual tracking and recognition using appearance-adaptive models in particle filters

IEEE Transactions on Image Processing

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Abstract

In this paper, we describe a cellular automata model for predicting biological spatiotemporal dynamics in an imagery data flow. The Bayesian probability-based algorithm is used to estimate the algal formation in a two-dimensional space. The dynamics of the cellular artificial life is described with diffusion, transport, collision and deformation. We tested the model with the historical data, including parameters, such as time, position and temperature.