2012, Vol.15, No.1, pp.70-73
In this paper we use Information Geometry tools to model statistically patterns arising in complex systems and describe their evolution in time. In particular, we focus on the analysis of images with medical applications and propose an index that can estimate the level of self-organization and predict future problems that may occur in these systems.
Key words:
Shapes, landmarks, complex systems, Information Geometry, Gaussian Mixture Models
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