Automata-based L-Grammar extraction from multiple images for virtual plants

L-system (Lindenmayer system) and its application have been one of the most famous and powerful tools for virtual plant modelling. But it is really hard to develop L-grammar manually for a given plant depending only on imagination or experience. For bridging this gap, a novel automatic L-grammar extraction approach is presented in this work. Initially, image processing as well as pattern recognition methods are employed to recover morphological and geometrical information for growth units and metamers. And then, these data are further analyzed using Markovian methods and acted as parameters for bidimensional hierarchical automata (BHA) to describe plant branching structure. Finally, the L-grammar has been extracted by means of the transformation from BHA to L-system. Experimental results show that our approach can extract L grammar for unfoliaged tree effectively.