图像特征点匹配的强壮算法

同一场景的不同图像匹配是计算机视觉中的一个基本问题，在诸如三维重度，对象识别和分类、图像对齐和相机自校正等应用中，特征匹配都是一个关键步骤，其中特征点匹配是较为常用的一种方法，特征点匹配的效果受到很多因素的影响，如景物的遮挡，光照和噪声等，变化很大，文中对结指标派算法进行扩以解决全局优化问题，并利用场景深度局部连续的条件作为附加约束，提出一种新的特征点匹配算法，整个算法只用到两次优化，而且几乎全部使用矩阵运算，效率比已有的算法高，实验表明该算法的效果是令人满意的。

Robust Algorithm for Feature Point Matching

Image matching is a key problem of computer vision and is frequently used in 3D model reconstruction, object recognition, image alignment, camera self calibration and so on. Feature point matching is the most common one among all kinds of image matching. Its matching result is affected greatly by many factors, such as object occlusions, lighting conditions and noises. In this paper we extend the standard assignment algorithm to solve extended assignment problem. It employs the condition that the depth of the scene is local continuous as extra constraint, and uses the method for extended assignment problem to do global optimization. Moreover, this algorithm only needs to carry out optimization twice and can be implemented almost completely with matrix computation, so its efficiency is higher than existing algorithms. Experiments show that the results of the algorithm are highly satisfactory.