Shape recognition based on the medial axis approach

We propose a novel, shape-matching algorithm using skeletal graphs. The topology of skeletal graphs is captured and compared at the node level. Such graph representation allows preservation of the skeletal graph´s coherence without scarifying the flexibility of matching similar portions of graphs across different levels. By using an appropriate sampling resolution, we are able to achieve a high recognition rate, and at the same time, significantly reduce the space and time complexity of matching. We tested our approach against the directed acyclic graph (DAG) method on noisy graphs and occluded or cluttered scenes. The results show that our approach is an effective and efficient technique for shape recognition.