摘要： 基于特征的图像匹配相关算法尽管已经十分普遍并得到广泛应用，但特征的提取容易受噪声影响。该文提出了一种用尺度空间下的临界特征点对图像进行匹配的方法。该方法采用尺度空间下的临界特征点来描述图像的灰度特征，对光照和噪声具有一定的鲁棒性。考虑到不同尺度下特征点对视觉影响的不同，算法用PTD距离对带权重的图像的特征点集进行匹配。由于PTD距离满足三角不等式规则，该算法适合于在大量数据库中快速检索及识别物体。实验证明了该算法的有效性。

关键词: 临界特征点, 尺度空间, 图像匹配

Abstract: Algorithms based on image features are very popular and widely used in image matching. However, the feature extraction process is often sensitive to noises. This paper presents an image matching algorithm using critical feature points in space-scale, which represent image gray-level feature. The algorithm is robust to the illumination intensity and noises. For the purpose of comparing distance between weighted feature points, the proportional transportation distance is used. Because PTD obeys the triangle inequality, the algorithm is suitable for efficient object retrieval and recognition in large database. Experiment result confirms the efficiency.

Key words: critical feature points, scale-space, image matching

中图分类号: 

• TP391