# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
## 3 Experimental Design and Results Analysis
### Experimental Result Analysis  
Figure 6 shows the disparity maps generated by SGM, CSCA + BOX, CSCA + GF and SGM-MSIF (this paper). In order to clearly reflect the stereo matching results, the mismatched pixels of different regions are marked with different colors in Figure 6, where magenta represents all regions (All), yellow represents non-occluded regions (Non-occ) and cyan represents discontinuous regions (Disc). It is observed that the \"magenta pixels\" are mainly concentrated in the occluded regions. The disparities of the occluded regions are difficult to obtain. The reason is that these regions have no corresponding pixels in the right image. However, generally, the number of mismatched pixels in Non-occ regions using SGM-MSIF is significantly less than those using the other three algorithms. In the low-texture regions of the images, the disparity maps obtained by SGM-MSIF is significantly improved compared with the other algorithms. In the Disc regions of Teddy and Tsukuba, the mismatched pixels are also obviously reduced in the SGM-MSIF results. From these red rectangles in Figure 6, we can see that our method successfully recovers the disparities of pixels not only in large homogeneous regions, but also in discontinuous regions. These experimental results state that the proposed algorithm of adding multi-scale information in \begin{table}
\begin{tabular}{c c c c c c} \hline \(\lambda_{cen}\) & \(\lambda_{cor}\) & \(\Pi_{1}\) & \(\Pi_{2}\) & \(\tau\) & \(\gamma\) \\ \hline
50 & 30 & 1.0 & 3.0 & 15 & 0.3 \\ \hline \end{tabular}
\end{table} 
Table 1: Parameter settings of SGM-MSIF. Figure 5: Middlebury stereo evaluation datasets. (**a**) Left images of the stereo pairs from four datasets: Cones, Teddy, Tsukuba and Venus. (**b**) Ground truth maps of these images. the multi-path cost aggregation can effectively increase the accuracy of the disparity map and reduce the EMR of low-texture regions. Tables 2-4 present the EMRs of the six algorithms in Non-occ, All and Disc regions, respectively. Figure 7 shows the histogram of EMRs in Non-occ regions of the six algorithms. In Table 2 and Figure 7, the proposed algorithm achieves the best average EMR and the smallest EMRs in Cones and Teddy datasets. Although the EMRs of our method in Tsukuba and Venus datasets are not the lowest, the differences between our approach and those of other algorithms are very small. It is difficult to get further improvement since the matching accuracies in these datasets are already high enough. In Non-occ regions, compared with traditional SGM and CSCA + GF (the best matching accuracy under CSCA [16]) algorithms, the average EMRs of SGM-MSIF reduced by 1.96% and 0.92%, respectively. In Tables 3 and 4, SGM-MSIF also achieves the best average EMRs and the smallest EMRs in three of the four datasets. Moreover, in Disc regions, compared with SGM and CSCA + GF, the average EMRs of SGM-MSIF are reduced by 6.68% and 1.68%, respectively. From Tables 2-4, compared with the other five algorithms, our algorithm has excellent matching accuracy in general. It indicates that the SGM-MSIF algorithm  
Figure 6: Disparity maps obtained by four different algorithms: (**a**) SGM (traditional SGM algorithm [11]); (**b**) CSCA + BOX (the fastest matching speed under CSCA [16]); (**c**) CSCA + GF (the best matching accuracy under CSCA [16]); and (**d**) SGM-MSIF (this paper). Magenta represents All error. Yellow represents Non-occ error and cyan represents Disc error. can effectively improve the matching accuracy and the robustness of the stereo matching algorithm in low-texture regions. Table 5 lists the running times of the six algorithms on different datasets. As shown in the table, the traditional SGM algorithm runs fastest, but its matching accuracy is the worst. Compared with CSCA + BOX and CSCA + GF, the average running time of SGM-MSIF algorithm reduced by 17.3% and 58.7%, respectively.