## 6 Experimental Results
### Computational Performance in Real Captured PCB Images  
Additional experiments were performed using real captured PCB images. The images were captured when an object was arbitrarily moving and rotating. All test and corresponding template images are shown in Figures 16-18. The sizes of the test images are 800 \(\times\) 600. This experiment assessed performance of computation and correctness according to the matching results. The matching results for the proposed method and that of references [15] and [21] are denoted by red, blue and green boxes, respectively. To allow a fair comparison, the comparison methods are also optimized using the image pyramid search framework. The statistical results for efficiency and the effect of pyramid levels are summarized in Table 5. Here it is shown that the computational burden is significantly reduced when the image pyramid search framework is used.  
Figure 16: Printed circuit board (PCB) case1 images. (**a**–**c**) real and (**d**) template image (200 \(\times\) 200 pixels).  
Figure 17: PCB case2 images. (**a**–**c**) real images and (**d**) template image (200 \(\times\) 200 pixels).  
Figure 19: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods Time** & \multicolumn{4}{c}{**Average Execution Time (s)**} \\ \cline{2-5}  & **Figure 16** & **Figure 17** & **Figure 18** & **Figure 19** \\ \hline \multicolumn{5}{c}{Pyramid levels = 2} \\ \hline Proposed method & 0.316 & 0.124 & 0.126 & 0.322 \\
[15] & 0.302 & 0.143 & 0.147 & 0.337 \\
[21] & 0.057 & 0.03 & 0.031 & 0.062 \\ \hline \multicolumn{5}{c}{Pyramid levels = 1} \\ \hline Proposed method & 1.721 & 0.766 & 0.765 & 1.731 \\
[15] & 3.701 & 1.393 & 1.401 & 3.842 \\
[21] & 0.755 & 0.364 & 0.366 & 0.767 \\ \hline \multicolumn{5}{c}{Pyramid levels = 0 (without image pyramid search technique)} \\ \hline Proposed method & 19.5 & 8.318 & 8.329 & 21.183 \\
[15] & 30.458 & 14.85 & 14.465 & 32.172 \\
[21] & 11.526 & 5.694 & 5.737 & 11.526 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The computational burden for the proposed and compared methods.  
Figure 18: PCB case3 images. (**a–c**) real images and (**d**) template image (200 \(\times\) 200 pixels).  
In these test cases, the mismatch for the method of [21] is clearly seen in the PCB test cases, such as Figure 16b,c, Figure 17b,c, and Figure 18b,c (shown as green boxes). This method is sensitive to test images when the template is located in a complex background. In terms of the method of [15], the matching results provide a more accurate localization of the template image in the test images (shown as blue boxes). However, the results for the rotation angle are not sufficiently precise, such as in Figures 16a,c and 19a-c. By contrast, the matching results for the proposed method are seen clearly in Figures 16-18. They are superior to the results for the other methods. On the other hand, the image pyramid searching technique is used to enhance the performance in terms of efficiency. It can been seen the computation advantage of the proposed method, the [15; 21] are around 66 times, 95 times, and 186 times.  
From the experimental results, the proposed method not only provides a correct location, but also estimates a correct and precise rotation angle for the template image in real captured PCB images. Based on the convincing results of this experiment, the proposed method can be used with real-world images. However, the feature-based matching method that use the SIFT descriptor is not appropriate for the complex images, such as Figures 16-19 because of the heavy computational burden. There are more than 2300 feature points in these real captured images. The computation times for the real captured cases are 642.22, 620, 448.28, and 489.1 msec, respectively. The efficiency of the feature-based method is slower than the proposed method.