## 3 Results and Discussion
### Evaluation of the Influences of Parameter Settings  
Selecting proper parameters is very important in local stereo matching methods. In this section, we explore the influence of different parameter settings. The main parameters in the cost computation step are regularization parameters \(\lambda_{GRAD}\) and \(\lambda_{CTg}\), which play an important role to adjust the proportion of two costs in the combined cost function. We chose five textureless images as test images and tuned the parameter individually with the rest of the parameters remaining constant. Figure 11a,b show the quantitative influence of parameters \(\lambda_{GRAD}\) and \(\lambda_{CTg}\) to disparity estimation in detail. From this figure, we can find that disparity results are stable with regularization parameters \(\lambda_{GRAD}\) and \(\lambda_{CTg}\) varying from 15 to 45. When \(\lambda_{GRAD}\) is set to 25 and \(\lambda_{CTg}\) is set to 15, the overall performance is relatively better, so \(\lambda_{GRAD}\) is recommended to be set to 25 and \(\lambda_{CTg}\) is recommended to be set to 15. In the step of cross-based support window construction, the color similarity threshold \(\tau_{1}\) and arm length threshold \(L_{1}\) were used to adjust the size of the support window for the richly textured regions. These two parameters have no significant influence on the disparity accuracy for most images, as shown in Figure 11c,d, since we set a small distance threshold (\(d_{Llim}=9\)) to distinguish the textureless region from the textured region. For the Lampshade2 data set, the parameter settings (\(\tau_{1}=30\), \(L_{1}=31\)) improved the disparity accuracy by about 2% in the discontinuous regions. The experimental results are more affected by tuning the color similarity threshold \(\tau_{2}\) and arm length threshold \(L_{2}\), which were \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Aloe** & **Baby1** & **Baby2** & **Baby3** & **Bowling1** & **Bowling2** \\ \hline GF & 7.407 & 2.575 & 5.534 & 5.981 & 7.94 & 12.184 \\ Proposed & 8.626 & 4.092 & 10.635 & 6.197 & 14.636 & 14.794 \\ \hline
**Algorithm** & **Cloth1** & **Cloth2** & **Cloth3** & **Cloth4** & **Flowperots** & **Lampshade1** \\ \hline GF & 2.96 & 8.613 & 3.94 & 8.393 & 12.405 & 11.223 \\ Proposed & 3.225 & 10.418 & 4.332 & 8.454 & 12.696 & 9.54 \\ \hline
**Algorithm** & **Lampshade2** & **Midd1** & **Midd2** & **Monopoly** & **Plastic** & **Rocks1** \\ \hline GF & 15.729 & 37.653 & 35.381 & 22.803 & 32.666 & 4.183 \\ Proposed & 8.57 & 13.857 & 16.27 & 7.335 & 25.724 & 4.968 \\ \hline
**Algorithm** & **Rocks2** & **Wood1** & **Wood2** & **Avg (all)** & & \\ \hline GF & 3.587 & 3.829 & 0.965 & 11.712 & & \\ Proposed & 3.973 & 8.574 & 0.484 & 9.4 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of bad pixels for the Middlebury 2006 dataset. Figure 10: Comparison of the original cost aggregation method with our proposed method for the Middlebury dataset. From top to bottom: Lampshade1, Lampshade2, Midd1, Midd2, Monopoly, Plastic. (**a**) Left images; (**b**) ground truth maps; (**c**) results of the local stereo matching method based on traditional guided filter; (**d**) error maps for the method based on traditional guided filter; (**e**) results of the proposed method; (**f**) error maps of the proposed method. used to determine the size of the adaptive support window in the large textureless regions. In this paper, we set the color similarity threshold \(\tau_{2}\) to 6 and arm length threshold \(L_{2}\) to 80 respectively to obtain good accuracy. In addition, the disparity results degraded gradually with the increasing distance threshold \(d_{Lim}\), so we selected a small distance threshold which was set to 9. The smooth parameter of the modified guided filter \(\varepsilon\) was set according to [23]. Figure 11: The experimental results on different parameter settings using the Middlebury textureless images.