An application of embedology to spatio-temporal pattern recognition

The theory of embedded time series is shown applicable for determining a reasonable lower bound on the length of test sequence required for accurate classification of moving objects. Sequentially recorded feature vectors of a moving object form a training trajectory in feature space. Each of the seq...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 1996-04, Vol.32 (2), p.768-774
Main Authors: Stright, J.R., Rogers, S.K., Quinn, D.W., Fielding, K.H.
Format: Article
Language:English
Subjects:
Aerospace testing
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Fractals
Hidden Markov models
Image sequences
Land surface
Military computing
Nearest neighbor searches
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Space technology
Vehicles
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Summary:The theory of embedded time series is shown applicable for determining a reasonable lower bound on the length of test sequence required for accurate classification of moving objects. Sequentially recorded feature vectors of a moving object form a training trajectory in feature space. Each of the sequences of feature vector components is a time series, and under certain conditions, each of these time series has approximately the same fractal dimension. The embedding theorem may be applied to this fractal dimension to establish a sufficient number of observations to determine the feature space trajectory of the object. It is argued that this number is a reasonable lower bound on test sequence length for use in object classification. Experiments with data corresponding to five military vehicles (observed following a projected Lorenz trajectory on a viewing sphere) show that this bound is indeed adequate.
ISSN:0018-9251
1557-9603
DOI:10.1109/7.489519