Microstructure characterization and reconstruction are important for both microstructure design representation and design evaluation. Design representation requires a finite set of quantitative descriptors of the infinite dimensional microstructure image as design variables, which can be obtained via microstructure characterization. In design evaluation, statistical FEA analysis of material properties [9,10] requires microstructure reconstruction to generate multiple random but statistically equivalent microstructures. Microstructure characterization describes the heterogeneous microstructure quantitatively with a small set of microstructure parameters based on image analysis. For given microstructure descriptions, microstructure reconstruction generates statistically equivalent digital microstructures for stochastic analysis of material behavior [11]. Three basic categories of microstructure characterization/reconstruction approaches are widely adopted in material analysis and design: (1) the physical descriptor-based approach, (2) the correlation function-based approach, and (3) the random field-based approach. In the physical descriptor-based approach, important structural parameters (descriptors) highly related to the interested material properties are chosen as microstructure representation. Typical microstructure descriptors fall into three categories: composition, dispersion status, and geometry of the inclusions. **Composition descriptors** indicate the percentage of each material constituent, such as volume fraction of filler. **Dispersion descriptors** depict the inclusions' spatial relation and their neighbor status, such as the nearest neighbor distance, the ranked neighbor distances, pair correlation [12-16], and spatial pattern descriptors obtained from the infinite dimensional *F*-, *G*-, and *K*-functions [17]. **Geometry descriptors** provide information of the shapes of inclusions such as the size/radius distribution, surface area, surface-to-area/volume fraction, roundness, eccentricity, elongation, rectangularity, tortuosity, aspect ratio, etc. [12,16,18-23]. The major strength of physical descriptors is the clear physical meanings they offer and the meaningful mappings to processing parameters [24,25].