中文摘要：

特征匹配是计算机视觉中的一个基本问题,基于特征点的特征匹配方法则是其中最为常用的一种算法,有着重要的研究意义和研究价值.众所周知,特征点匹配的结果受很多因素的影响.为更好地处理视角变换的特征匹配问题,给出一种基于特征点位置关系的几何约束匹配方法.即通过引入新近发现的射影不变量——特征数,构建特征点位置间的几何信息描述子;进一步建立每个点的特征数直方图并使用巴氏系数度量几何相似度;最后在基于纹理特征描述子基础上增加文中所给出的几何信息描述子获得特征匹配的约束条件.实验结果证明,该算法可以有效的提高特征点匹配的精度,同时对视角变化较大及纹理相似的情况具有很好的匹配效果.

英文摘要：

Feature matching is a basic problem in computer vision, and the method based on feature point is the most common one which has important research value. There are many factors which may affect the matching result of the feature points. In this paper, a novel matching method combined texture information and geometric constraints under projective transformation is proposed. First, the newly developed projective invariant ＂characteristic number＂ is introduced to compute the geometric descriptors, then histograms of characteristic numbers for each point are built and the Bhattacharyya distance is used to measure the geometric similarities. Finally, the geometric constraints are applied to the descriptors based on local texture information to generate the criteria of points matching. Experiment results show this method can improve the matching accuracy effectively. It also performs well against large viewpoint changes and senses with similar textures.