利用Hough变换的匹配对提纯

目的 针对传统的匹配对提纯算法存在容错性差、效率低等问题,提出了一种利用Hough变换的匹配对提纯算法。方法 假设正确的匹配对一致性地服从一个变换模型。首先,为两幅图像的变换关系选择一个合适的数学模型,利用Hough变换确定模型方程参数的解。然后检验原始匹配对,保留符合模型方程的匹配对,从而达到提纯的目的。结果 与传统的RANSAC(random sample consensus)等算法相比,本文算法具有更高的容错率、召回率与更优的运行效率,且是稳定的。实验结果表明,在误配率低于85%时算法能完全剔除误匹配,且误配率高达95%时依然有50%的可能性成功剔除误匹配。结论 把Hough变换引入到匹配对提纯的应用中,该算法在所选模型准确或近似准确的情况下能鲁棒地提纯匹配对。由于模型方程参数个数决定参数空间维数,维数高导致投票及搜索最大值点的时间、空间复杂度大,因此该算法适用于模型参数较少(不大于4)的情况。

Purifying algorithm for rough matched pairs using Hough transform

Objective Outliers inevitably exist in image matching, and they may result in a significantly high mismatching ratio when the overlapping region of two images is small. Some normal matching algorithms cause a high mismatching ratio. A robust purifying algorithm for rough matched pairs can be designed to solve this problem by reducing difficulties in image matching. Since Hough transform was proposed, it has been used to detect certain kinds of curves. It establishes a mathematical model for curves and votes in the para-meter space and determines exact parameters of the curve via maximum value in the parameter space. Based on the same voting idea, Hough transform is introduced in this paper to purify rough matched pairs. Method First, we assume that those truly matched pairs obey a certain transform model equation. Then, a common transform model can be established, and Hough transform is used to obtain parameters of model equation. In particular, each matched pair votes on the corresponding hypersurface, which is in the parameter space and determined by Hough transform. Thus, parameters of the transform model equation can be determined by the global maximum value in the parameter space. Then, all matched pairs that obey model equation are saved. Thus, the rough matched pairs can be purified in this way. Result Compared with traditional algorithms, such as random sample consensus, the proposed algorithm is not only robust to outliers with a good recall ratio but also more efficient. Moreover, experimental results indicate that the proposed method can be robust when the ratio of outliers is as high as 85%, and even when the ratio is up to 95%, it still can work very well with a probability of 50%. Conclusion Hough transform can be applied to purify matched pairs, and many experiments prove its feasibility. Corresponding models should be chosen to obtain a good performance when aiming at rigid-body transformation and affine transformation. However, the proposed method is not suitable when many parameters (more than four) exist in the model equation, given that a high-dimensional space determined by parameters of the model equation is memory expensive and time consuming when searching and voting in the high dimensional parameters space.