Abstract
We propose an adaptive pyramidal approach using local feature for orientation code matching (OCM). OCM can achieve robust image matching in outdoor environment, and its high computational costs can be reduced by using pyramidal approach. However, as the pyramid levels deepen, the mismatch between pyramid generation method and types of scene tends to cause the decline of matching success rate. To avoid this problem, we broadly classify the image features into two types: artificial features or not. And we define a new degree of artificial structure based on local orientation code distribution. In this way, we obtained an adaptive selection of pyramid generation method according to the local image features. Our experimental results using aerial images showed that our method worked well and improved the success rate at the upper pyramid levels.
