# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 4 Experimental Results and Analysis
### Experiment Set 2  
According to Figure 2e, white architecture is the most conspicuous object. All the stitching effects are fine, because the edges of white building are aligned. However, the stitch lines obtained from VN, LD and VM cut through the high-saliency object, which is dangerous to the mosaic image quality. Then we change the parameters of Equation (9) and make comparison of these stitching effects, as shown in Figures 5 and 6. The broken edges in the yellow box of Figure 5d debase the image quality. The stitch lines in (e) and (f) avoid the white building and unaligned edges, and the final result is seamless. Evaluations of fusion effects are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Images**} & \multicolumn{2}{c}{**Canny (\(\sigma\) = 0.01)**} & \multicolumn{2}{c}{**Canny (\(\sigma\) = 1)**} \\ \cline{2-5}  & \(\mathbf{N}\) & \(\mathbf{P}\) & \(\mathbf{N}\) & \(\mathbf{P}\) \\ \hline a & 722 & 0.7490 & 306 & 0.3174 \\ b & 415 & 0.4305 & 157 & 0.1629 \\ d & 285 & 0.1495 & 101 & 0.0530 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluations of fusion effects in Figure 4.  
Figure 5: The stitch line results with different weight map: (**a**) VN, (**b**) LD, (**c**) VM, (**d**) VS, (**e**) WM with parameter set 0.1, 0.2, 0.2, 0.5, and (**f**) 0.1, 0.1, 0.25, 0.55.  
According to the table, the six mosaic images are visual pleasant even the high-saliency objects are cut through by stitch lines. That is because the objects are registered perfectly. However, in other cases, this kind of stitch lines may cause many unexpected effects (shown in Figure 6).  
Figure 6 shows stitch lines using different combinations of visual properties. There are some unnatural edges caused by breaking the reflections of the sun in the rivers indicated by yellow boxes in Figure 6a,b. Due to the high saliency of the sun reflections, the stitch effects are poor. The dislocation in Figure 6c is also an unexpected item. Figure 6d-f are acceptable outcomes without generating broken  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. of Images**} & \multirow{2}{*}{**Weight Map**} & \multicolumn{2}{c}{**Canny (\(\sigma\) = 0.01)**} & \multicolumn{2}{c}{**Canny (\(\sigma\) = 1)**} \\ \cline{2-6}  & & \(N\) & \(P\) & \(N\) & \(P\) \\ \hline a & VN & 339 & 0.2180 & 98 & 0.0630 \\ b & LD & 297 & 0.1820 & 60 & 0.0368 \\ c & VM & 252 & 0.1622 & 49 & 0.0315 \\ d & VS & 286 & 0.1974 & 60 & 0.0414 \\ e & parameter set 1 & 334 & 0.1742 & 63 & 0.0329 \\ f & parameter set 2 & 268 & 0.1682 & 73 & 0.0458 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluations of fusion effects in Figure 5.  
Figure 6: The stitch line results with different weight map: (**a**) VN, (**b**) LD, (**c**) VM, (**d**) VS, (**e**) WM with parameters 0.1, 0.2, 0.2, 0.5, and (**f**) 0.1, 0.1, 0.25, 0.55.  
strong edges. The evaluations of these images are listed in Table 4. Although indicators of (a), (b) and (c) are at a low level, they are unnatural mosaic images because some of detected edges are strong. According to the table, (d), (e) and (f) have similar performance.