基于信息测度特征和Hausdorff距离的图像匹配策略

由于传统的图像匹配方法受到诸如景物的遮挡、光照和噪声的影响比较大，并且需要建立模板与图像间的对应关系，因而使实际图像的匹配变得困难。为了克服上述缺陷，以便快速地进行图像匹配，通过引入信息测度概念来提取边缘特征点，并基于修正后的：Hausdorff距离构造相似性测度，提出了一种基于信息测度和：Hausdorff距离的图像匹配策略。该策略不仅加快了匹配过程，提高了抗噪性能，而且能准确匹配遮挡图像，从而较好地解决了基于传统Hausdorff距离的图像匹配因噪声点、伪边缘和出格点而造成的误匹配问题。实验结果证明，该方法是正确有效的。

An Image Matching Strategy Based on Information Measures and Hausdorff Distance

The conventional matching methods are easily affected by scene occlusions, light and noise. On the other hand, the relationship of correspondence between model and image need to be built, which makes the matching process become more complicated. Then Hausdorff distance is used and the drawbacks of conventional partial Hausdorff distance is analyzed and corrected. To achieve image matching quickly, the concept of information measures is introduced into image matching to extract the edge characteristic points based on edge detection, and the similarity measures are constructed based on modified Hausdorff distance, then a new matching strategy is proposed based on information measures and Hausdorff distance. In this method, a process of pre-matching is used to pick out the unimportant regions by making use of some general information, such as the proportion of pixels' number in the range of preset gray level or preset information measure value, which speeds up the matching process greatly. The proposed strategy improves the resistance to noise and gives the criteria of parameter selection to some extent. In addition, this method matches the image occlusions correctly and overcomes the mismatching problems that induced by noise, spurious edge segments and outlier points. The experimental results demonstrate that the proposed strategy is feasible and effective.