## 6 Experiments
### Different Number of Cameras  
The EEMM method was originally designed for merging four disparity maps obtained with the use of four stereo cameras considered in EBCA. However, this paper describes the possibility of using the method with EBCA arrays in which the number of side cameras is lower than four. Merging less than four disparity maps can be perceived as merging four disparity maps such that there are blank maps in which all values of disparities are not determined. The quality of results obtained by using Exception Excluding Merging Methods depends on the number of cameras included in EBCA as presented in Figure 12. Quality metrics considered in this section are the same as those considered in the previous section. The figure presents average results (AVG) and results for ELAS, GC Expansion, StereoSGBM and MC-CNN algorithms. Experiments were performed for the number of cameras ranging between two and five. Experimental sets differ in the number of side cameras. The same central camera was used in every configuration. Experimental data for tested arrays were acquired by removing images from a full set of images acquired from the array with all five cameras. The usage of two cameras is equivalent to using a single stereo camera. There are four possibilities of obtaining such a configuration from the complete EBCA. In each case, a central camera and one of four side cameras is used. The results presented in Figure 12  
Figure 11: Disparity maps obtained using a pair of cameras (**a–d**) and EBCA (**e–h**) for the following stereo matching algorithms: ELAS (**a,e**), StereoSGBM (**b,f**), GC Expansion (**c,g**) and MC-CNN (**d,h**). Figure 10: Values of the COV metric when EEMM was used with algorithms: (**a**) ELAS, (**b**) StereoSGBM, (**c**) GC Expansion and (**d**) MC-CNN. were based on calculated the average mean of four different results obtained from four camera pairs possible to select from a set with five cameras. This kind of calculating the average mean of results obtained from all possible subsets was also applied in every other configuration. Configuration 3P means that there are three cameras placed along a straight line. Such a configuration can be acquired from a complete EBCA set by selecting cameras located either along a horizontal line or along a vertical line. Configuration 3L also consists of three cameras however they are arranged in the L-configuration, i.e., cameras are in locations corresponding to vertices of a isosceles rectangular triangle. The purpose of distinguishing between configurations 3L and 3R is to verify whether differences in the arrangement of three-camera EBCA influence the quality of results. Configuration 4 is the one in which one camera was excluded from a full set of five cameras. Configuration 5 consists of all cameras used in the tested EBCA. Figure 12 visualizes that the increase in the number of cameras reduces the value of the BMP metric, which means that the quality of results become higher. When two cameras are used, all tested stereo matching algorithms provided disparity maps with the value of BMP in the range between 17.9% and 24.85%. The greatest influence on the results caused by increasing the number of cameras occurred for the ELAS algorithm. The final result was equal to 12.68% with the use of five cameras. Thus, results improved 48.97% of its value obtained for a single pair of cameras. On average the improvement was equal to 40.79%. The MC-CNN algorithm produced results for which BMP was the lowest in the configuration with a single pair of cameras. These results were even further improved by increasing of the number of cameras included in the array. However, it was the ELAS algorithm which generated results with the lowest value of BMP when five cameras were used. Results for MC-CNN used with the same number of cameras were only slightly worse as the difference in values of BMP for these two algorithms was equal to 0.07%.