Inferring shape evolution

Dynamic shapes, namely shapes that change with time, represent an important issue in several scientific and technological contexts. The current article presents a model-based mathematic-computational approach for inferring the processes governing some of the most representative types of shape evolution, with special attention given to biological shapes. The considered models include functional mappings, convolution-based evolution and normal wavefront propagation. The methods are illustrated with respect to stationary (global) and non-stationary (local) dynamic evolutions, and the obtained results substantiate the potential of the presented methodology. Although concentrating on 2D shapes, the reported results can be extended to higher dimensional objects.