## 4 Conclusions  
This paper proposes a 3D object segmentation and labeling algorithm for static image pairs. The proposed algorithm includes four processing steps, the texture construction edge detection algorithm, distance connected component algorithm, object extension and merge algorithm, and object segmentation. The proposed algorithm can not only solve the segmentation and labeling problems of complex overlapping objects but also estimate the distance and size of objects that can be used as reference information for subsequent object recognition. The experimental results are verified using the test images of the KITTI and Middlebury datasets in different outdoor and indoor scenes. In addition, the paper develops a dual-camera system to capture images and measured the actual distance and size of objects in the images that are used as references for the data estimated by the proposed algorithm. The experimental results show that the proposed algorithm can effectively solve the segmentation and labeling problems of complex overlapping objects in complex scenes with different environments and obtain estimation results of the distance and size of each object. The estimated object size can be further used as important data to identify the \begin{table}
\begin{tabular}{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 & 115 & \(7\times 4\) & 112 & \(7\times 4\) & 97.4 & 100.0 & 100.0 \\ \hline
2 & 132 & \(4\times 13.5\) & 127 & \(5\times 13\) & 96.2 & 75.0 & 96.3 \\ \hline
3 & 167 & \(20\times 14\) & 162 & \(20\times 17\) & 97.0 & 100.0 & 78.6 \\ \hline
4 & 340 & \(25\times 52\) & 330 & \(10\times 57\) & 97.1 & 40.0 & 90.4 \\ \hline
5 & 143 & \(4\times 13.5\) & 137 & \(7\times 14\) & 95.8 & 25.0 & 96.3 \\ \hline
6 & 113 & \(41\times 28\) & 123 & \(51\times 33\) & 91.9 & 75.6 & 82.1 \\ \hline
7 & 451 & \(53\times 158\) & 422 & \(13\times 149\) & 93.6 & 24.5 & 94.3 \\ \hline
8 & 260 & \(78\times 47\) & 240 & \(69\times 56\) & 92.3 & 88.5 & 80.9 \\ \hline
9 & 164 & \(14\times 16\) & 155 & \(16\times 18\) & 94.5 & 85.7 & 87.5 \\ \hline
10 & 169 & \(21\times 14\) & 162 & \(21\times 16\) & 95.9 & 100.0 & 85.7 \\ \hline
11 & 110 & \(10\times 11\) & 112 & \(11\times 12\) & 98.2 & 90.0 & 90.9 \\ \hline
12 & 132 & \(6\times 15\) & 126 & \(8\times 16\) & 95.5 & 66.7 & 93.3 \\ \hline
13 & 395 & \(22\times 36\) & 377 & \(26\times 35\) & 95.4 & 81.8 & 97.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Three-Dimensional Information Table of Segmented Objects in Test Image 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Image Resolution (pixels)** & **Time (ms)** \\ \hline Test Image 1 & 1242 \(\times\) 375 & 16.2 \\ \hline Test Image 2 & 1224 \(\times\) 370 & 16.7 \\ \hline Test Image 3 & 2964 \(\times\) 2000 & 31.4 \\ \hline Test Image 4 & 1920 \(\times\) 1080 & 26.6 \\ \hline Test Image 5 & 1920 \(\times\) 1080 & 27.5 \\ \hline Test Image 6 & 1920 \(\times\) 1080 & 27.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The processing times of the proposed algorithm. object category. For example, if an object is identified as a person but the height estimated by this algorithm is 32 cm, then we can easily determine the object as a character model. The proposed algorithm can be widely applied to visual detection techniques, which are required in the development of intelligent vehicles or artificial intelligence-related research. Conceptualization, W.-C.L. and C.-C.C. ; methodology, W.-C.L. and C.-C.C. ; software, W.-C.L. ; validation, W.-C.L., C.-C.C. and J.-H.Y. ; formal analysis, W.-C.L. ; investigation, W.-C.L. ; data curation, W.-C.L. ; writing--original draft preparation, W.-C.L. ; writing--review and editing, C.-C.C. and J.-H.Y. All authors have read and agreed to the published version of the manuscript.