The minimum spanning tree (MST) based non-local cost aggregation algorithm performs well in accuracy and time efficiency. However, it can still be improved in two aspects. First, we propose a logarithmic transformation on matching cost function to improve the matching efficiency in texture less regions. The textureless neighbors can provide effective contributions in cost aggregation by the proposed monotone increasing function. Hence the algorithm can distinguish different pixels in textureless regions. Second, MST algorithm only utilizes color information in weight function while aggregating, which leads 3D cues missing. We introduce depth weight computed from the original MST algorithm into an edge weight function. With the proposed color-depth weight, we further iteratively rebuild the tree and obtain enhanced disparity map. Performance evaluations on 19 Middlebury stereo pairs and Microsoft stereo videos show that the proposed algorithm outperforms than other five state-of-the-art cost aggregation algorithms.

Segment-tree (ST) based cost aggregation algorithm for stereo matching successfully integrates the information of segmentation with non-local cost aggregation framework. The tree structure which is generated by the segmentation strategy directly determines the final results for this kind of algorithms. However, the original strategy performs unreasonable due to its coarse performance and ignores to meet the disparity consistency assumption. To improve these weaknesses we propose a novel segmentation algorithm for constructing a more faithful ST with enhanced segmentation advantage according to a robust initial over-segmentation. Then we implement non-local cost aggregation framework on this new ST structure and obtain improved disparity maps. Performance evaluations on all 31 Middlebury stereo pairs show that the proposed algorithm outperforms than other five state-of-the-art aggregated based algorithms and also keeps time efficiency.