## 3 Experimental Results
Test image 6 photographed by the self-made image capture system is an indoor test image. The experimental results of Test image 6 are shown in Figure 22. Figure 22a is the image pair of Test image 6. The baseline is set to 50 mm. The rest of the hardware specifications are the same as those for Test image 5. Regarding relevant parameters, the maximum detection distance is 4.5 m, the separation distance is 10 cm, and the minimum reserved area is 25 cm\({}^{2}\). Figure 22b shows the detection result of the object contour. The object segmentation result is shown in Figure 22c. The background image is shown in Figure 22d. A total of 13 different foreground objects are detected and segmented from Test image 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multicolumn{2}{c}{**Actual Measured Data (cm)**} & \multicolumn{2}{c}{**Object Information (cm)**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{2-7}  & **Distance** & **Width** \(\times\) **Height** & **Distance** & **Width** \(\times\) **Height** & **Distance** & **Width** & **Height** \\ \hline
1 & 2200 & - & 1980 & \(155\times 225\) & 90.0 & - & - \\ \hline
2 & 3800 & - & 3516 & \(375\times 284\) & 92.5 & - & - \\ \hline
3 & 4000 & - & 3726 & \(422\times 830\) & 93.2 & - & - \\ \hline
4 & 3400 & 433 \(\times\) 149 & 3162 & \(488\times 109\) & 93.0 & 87.3 & 73.2 \\ \hline
5 & 1800 & 439 \(\times\) 154 & 1806 & \(433\times 144\) & 99.7 & 98.6 & 93.5 \\ \hline
6 & 3900 & - & 3726 & \(253\times 732\) & 95.5 & - & - \\ \hline
7 & 900 & 80 \(\times\) 30 & 858 & \(98\times 26\) & 95.3 & 77.5 & 86.7 \\ \hline
8 & 3500 & - & 3516 & \(313\times 133\) & 99.5 & - & - \\ \hline
9 & 3800 & - & 3726 & \(467\times 912\) & 98.1 & - & - \\ \hline
10 & 900 & \(65\times 105\) & 870 & \(95\times 98\) & 96.7 & 53.8 & 93.3 \\ \hline
11 & 1400 & \(55\times 172\) & 1470 & \(78\times 172\) & 95.0 & 58.2 & 100.0 \\ \hline
12 & 3600 & 200 \(\times\) 800 & 3516 & \(109\times 706\) & 97.7 & 54.5 & 88.3 \\ \hline
13 & 3900 & - & 3726 & \(448\times 920\) & 95.5 & - & - \\ \hline
14 & 3400 & \(454\times 146\) & 3330 & \(481\times 138\) & 97.9 & 94.1 & 94.5 \\ \hline
15 & 3700 & - & 3516 & \(187\times 186\) & 95.0 & - & - \\ \hline
16 & 4000 & - & 3726 & \(356\times 968\) & 93.2 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Three-Dimensional Information Table of Segmented Objects in Test Image 5. Figure 22: The result of Test image 6. (**a**) Image pair; (**b**) Object edge contour; (**c**) Object segmentation; (**d**) Background image. Thirteen foreground objects, as shown in Figure 22c, are segmented from Test image 6. The distance and size of the foreground objects are estimated by the proposed algorithm, which are listed in the sequence in Table 7. From the experimental results, it can be observed that the complex overlapping objects in the image can be effectively detected and segmented. The actual measurement of the objects and the object information estimated by the proposed algorithm are listed in Table 8. Because the baseline used in the indoor scenes is short, the estimated object information for indoor objects is relatively close to the actual measured data. From Tables 6 and 8, the results for the height seem to be more accurate than for the width. According to our careful analysis of the reasons, we find there are two causes to affect the accuracy of the measured size. The two causes are the detection error and the oblique problem. The detection error occurs when the detected pixel number of the height or width is incorrect. When the detection error occurs, the oblique problem of the object will influence the accuracy of the measured width seriously. In this paper, we define a distance resolution \(\Delta Z\) to represent the difference in depth when the disparity value only changes one pixel under different distances. The value of \(\Delta Z\) is increased when the distance of the object is away from the cameras.