基于自适应迭代松弛的立体点对匹配鲁棒算法

图像匹配是立体视觉的重要部分，也是双目立体测量系统必须解决和最难解决的问题。为了对图像进行鲁棒性匹配，提出了一种基于自适应迭代松弛的立体点对匹配方法。该方法首先利用视差梯度约束来构造匹配支持度函数；然后通过松弛方法优化该函数来完成立体点对的匹配。由于利用了动态更新松弛匹配过程参数的方法，因此有效地降低了误匹配率和误剔除率。在此基础上还提出了对松弛过程结束后的匹配结果，再次使用视差梯度约束来进行进一步检验的策略，该策略能够以一定幅度的误剔除率提升为代价，大幅度降低了误匹配率，从而可满足许多要求严格限制误匹配率的应用。实验结果证明，该新算法是有效的，并已经用于一个双目立体测量原型系统当中。

A Robust Stereo Matching Algorithm Based on Adaptive Relaxation

Establishing stereo image correspondence is a vital and the most difficult problem for binocular stereo measure system. An adaptive relaxation algorithm for dealing with feature point stereo matching is proposed. In this algorithm, a correspondence support function is constructed based on disparity gradient limit, and the matching can be achieved through a relaxation optimizing procedure. A new scheme for dynamically updating the relaxation parameter during the matching procedure is introduced, by which the FAR (false acceptance rate) & FRR (false rejection rate) can be significantly reduced compared with other algorithms with parameters being fixed. After the relaxation procedure, the disparity gradient limit is re-imposed to further filter out false correspondences. It has been validated by experiments that this strategy can efficiently reduce FAR at a modest cost of increase in FRR, which meets the requirement of restrict FAR limitation in many industrial applications. The algorithm has been used in a binocular stereo measurement prototype system, and its robustness and effectiveness is affirmed by subsequent stereo reconstruction.