6533b826fe1ef96bd128524e
RESEARCH PRODUCT
Microstructure reconstruction using entropic descriptors
R. Piaseckisubject
FOS: Computer and information sciencesStatistical Mechanics (cond-mat.stat-mech)General MathematicsComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionGeneral EngineeringGeneral Physics and AstronomyBinary numberInverseFOS: Physical sciencesBinary patternGrayscaleImage (mathematics)CorrelationCorrelation function (statistical mechanics)Computer Science::Computer Vision and Pattern RecognitionStochastic optimizationStatistical physicsCondensed Matter - Statistical MechanicsMathematicsdescription
A multi-scale approach to the inverse reconstruction of a pattern's microstructure is reported. Instead of a correlation function, a pair of entropic descriptors (EDs) is proposed for stochastic optimization method. The first of them measures a spatial inhomogeneity, for a binary pattern, or compositional one, for a greyscale image. The second one quantifies a spatial or compositional statistical complexity. The EDs reveal structural information that is dissimilar, at least in part, to that given by correlation functions at almost all of discrete length scales. The method is tested on a few digitized binary and greyscale images. In each of the cases, the persuasive reconstruction of the microstructure is found.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-10-10 |