熵率超像素分割一致性检验视差细化算法

针对传统分割一致性检验视差细化算法处理低纹理图片时优化效果较差的问题，提出一种基于熵率超像素分割的改进方法，使用基于熵率的超像素分割算法代替均值漂移（Mean-shift）分割算法。针对参考图像进行超像素分割处理；将每一个分割块进行统计分析，根据集中趋势值筛选可信值与不可信值；进行视差填充处理获得最终优化后的视差图。选取15组Middlebury数据集中的图像对进行视差图获取并检测。实验结果表明，基于熵率超像素分割的改进方法对于低纹理图片和纹理复杂的图片都有着较好的优化效果，该算法平均误匹配率较传统算法最多降低了5.88个百分点。

Disparity Optimization Algorithm Using Entropy Rate Super-Pixel Segmentation Consistency Check

Aiming that the accuracy problems in texture lacking images when using the traditional approach, an improved method based on entropy rate super-pixel segmentation is proposed. The Mean-shift algorithm is replaced by the entropy rate super-pixel segmentation algorithm. Firstly, the reference image is segmented into super pixels. Then, statistical analysis is carried out for each segment so that the trusted and untrusted values can be screened according to the central tendency value. Finally, the disparity map is obtained by holes filling. Fifteen groups of images in Middlebury datasets are selected for disparity acquisition and detection. The experimental result shows that, this improved method has a good optimization effect for both texture lacking images and complex texture images, the average mismatching rate of this algorithm is 5.88 percentage points lower than that of the traditional algorithm.