## 6 Experiments
### EEMM Parameters  
The possibilities of selecting parameters \(B\) and \(Q\) in the EEMM method (Section 3.1) and their influence on the quality of disparity maps is presented in Figures 8-10. Figure 8 shows values of the BMP metric when \(1\leq B\leq 5\) and \(1\leq Q\leq 20\). The figure contains four parts showing results of using EEMM with four different stereo matching algorithms which are ELAS, StereoSGBM, GC Expansion and MC-CNN. The usage of MC-CNN was based on the _KITTI 2015 accurate_ trained network provided by the author of the MC-CNN algorithm. Results presented in charts are average values obtained from all six data sets included in the testbed. Average values were calculated with respect to differences in sizes  
Figure 7: The comparison between results based on images without self-calibration and results based on images with self-calibration.  
of matching areas presented in Table 1. The Y axes in Figure 8 refer to values of the BMP metric. The X axes correspond to values of the parameter \(Q\). Each chart contains five lines which depict results for different values of \(B\). The algorithm GC Expansion is influenced by random values and it generates different results for subsequent executions with the same input parameters. Therefore, results presented in figures for this matching method are average values calculated from ten executions of the algorithm.  
Figure 8 shows that in case of all tested stereo matching algorithms the greater value of the parameter \(B\) results in lower values of the BMP metrics. It is an advantageous effect as low values of BMP indicate higher quality of maps. Experiments were also performed for greater values of \(B\) however the increase of this parameter did not cause significant the decrease of BMP. As far as the parameter \(Q\) is concerned, the increase of its value also causes the decrease of BMP. Note that for algorithms ELAS and StereoSGBM the improvement is the most significant in the range of \(B\) between 1 and 5. Greater values of \(B\) also cause that BMP become lower however this effect is not as significant as in case of \(B\leq 5\). Such a characteristic does not occur with the GC algorithm in which there is a steady increase of the BMP values for the entire range of tested values of \(B\). As far as MC-CNN algorithm is concerned, parameters B and Q affect values of BMP to an even lesser extent than in case of GC Expansion. Note that in the Y axis there is a range between 98.62% and 99.66% for GC Expansion and the range is only between 99.93% and 99.99% for MC-CNN.  
Values of metrics BMB and COV were also acquired using the same input arguments as in case of the BMP metric. Results are presented in Figures 9 and 10. A relation between parameters of EEMM and values of the COV metric is similar as in case of BMP. The increase in values of both \(B\) and \(Q\) results in obtaining more advantageous values of COV.  
Figure 11 presents sample disparity maps obtained using a single stereo camera and EBCA. In the upper part of the figure there are four disparity maps retrieved on the basis of two cameras using algorithms ELAS, StereoSGBM, GC Expansion and MC-CNN, respectively [12; 39; 40; 41; 42]. The lower part of the figure contains disparity maps which were obtained when these algorithms were applied to EBCA with the use of EEMM. Parameters \(B\) and \(Q\) used in Exceptions Excluding Merging Method were set to 5.