An Image Matching Strategy Based on Information Measures and Hausdorff Distance
基于信息测度特征和Hausdorff距离的图像匹配策略

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.