基于改进代价计算和视差候选策略的立体匹配

立体匹配算法在图像弱纹理区和重复纹理区存在匹配困难、误差大的问题,为此提出一种基于改进代价计算和视差候选策略的立体匹配算法。首先结合改进的Census变换和自适应加权融合的双向梯度信息来计算初始匹配代价,提高代价计算的可靠性。其中:为传统Census变换增加内圈编码,提高邻域信息利用率,同时降低噪声的影响;利用自适应权重函数融合横向和纵向梯度代价,降低物体边缘区域的误匹配率。其次,采用自适应十字交叉窗口进行代价聚合,并通过建立候选视差集和引入邻域视差信息的方法来获取初始视差。最后通过两轮插值策略优化视差。实验结果表明,所提算法能够提高弱纹理区和重复纹理区的匹配效果,在Middlebury中4幅标准立体图像对的平均误匹配率为5.33%。

Stereo Matching Based on Improved Cost Calculation and a Disparity Candidate Strategy

Matching difficulty and the occurrence of large errors in the weak and repeated texture areas of an image are the problems associated with the stereo matching algorithm.To solve these problems,this paper proposes a stereo matching algorithm based on improved cost calculation and a disparity candidate strategy.First,the improved Census transform and adaptive weighted bidirectional gradient information are combined to estimate the initial matching cost,improving the reliability of cost calculation.Here,inner circle coding is added to the traditional Census transform for improving the utilization of neighborhood information while reducing the impact of noise.The adaptive weight function is used to combine the horizontal and vertical gradient costs for reducing the mismatching rate of the object edge areas.Second,after cost aggregation with an adaptive cross-window,the initial disparity can be obtained by establishing candidate disparity sets and introducing neighborhood disparity information.Finally,the disparity is optimized via two-round interpolation.Experimental results demonstrate that the proposed algorithm can improve the stereo matching of the weak and repeated texture areas and that the average mismatching rate on four standard stereo image pairs in Middlebury is 5.33%.