Recognition of Target Graphs from Images Using a Genetic Algorithm with Hill Climbing Searching

Abstract

We are developing a recognition system which aims to convert a sequence of illustrations in origami drill books into a 3D animation, with a view to providing an easy way for learning and enjoying origami art. As a part of this work, this paper proposes a method that recognizes a target graph corresponding to an origami shape from an origami illustration image. The target graph, called ISG (Ideal Shape Graphs), generated from an internal model that maintains 3D information about changes of origami states during the interpretation for the whole folding process. In order to understand the folding operation applied to the origami at each step, an ISG of the origami generated at current step has to be matched with the image of the origami illustration at the next step, to find whether there exists a similar graph within it. The image of the origami is usually very noisy. Since the computational cost to do this work is prohibitive even for problems of moderate sizes, we adopt a genetic algorithm in conjunction with hill climbing search technique to reduce the complexity. Experimental results show that good solutions can be found with lower cost using our method.