# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 4 Experimental Results and Analysis
### Experiment Set1  
Figure 2 shows the image pair and the quantitative results of the overlap region with 4 different HVS properties, which are VN, LD, VM, and VS, respectively. As is shown in Figure 2a, the two pictures are stitched at the boundary directly, where there is a dividing line visible to the naked eye. In the experiment, Figure 2b,c illustrate the visual discrimination of luminance through the VN and LD map of the same image in which Figure 2c shows the discrimination of edge information. In the VN map shown in Figure 2b, brightness discrimination of human eyes decreases as the _VN_ value increases, and in the LD map shown in Figure 2c, bright pixels represent small luminance difference between the two images, and small luminance gap can be smoothed by our technique. In the VM map, white areas represent smooth areas without edges and gray areas are texture areas, while the black areas in overlap zone contain strong edges, which are dangerous to mosaic image quality. The result of visual attention is pictured in Figure 2e. HVS will capture the white objects in this picture the first time. The last image of Figure 2 is the weight map combined by Figure 2b-e for tracing the stitching line. We compare stitching results of 4 different methods in Figure 3. The edges in yellow circle are broken by the left image boundaries, and the boundaries are visible. The multi-band blending is useless to eliminate discontinuous edges; it is to say that Figure 3b is a mosaic picture with low quality even whose boundaries are feathered. The red line in Figure 3c is the stitching line detected by the proposed method. It can be clearly seen in the enlarged yellow frame that this stitching line successfully locates  
Figure 2: The results with different human visual system (HVS) properties. (**a**) Stitched image after affine transformation; (**b**) image just with visual non-linearity (VN); (**c**) image just with luminance difference (LD); (**d**) image just with visual masking property (VM); (**e**) image just with visual saliency (VS); (**f**) image with the integral HVS. a low luminance difference path and avoids the high-saliency areas. Therefore, our method produces a satisfactory result without obvious stitching lines or discontinuous edges shown in Figure 3d. Due to the lack of universal no-reference image quality assessment based on HVS, two quantitative indicators were proposed to evaluate the stitching effects. One is \(N\) defined as the number of the pixels in the stitching line which can be detected by an edge detection algorithm (Canny), excepting those masked ones. The other indicator \(P\) is expressed as the following Equation (11):  
\[P=\frac{N}{N_{stitch\_line}}, \tag{11}\]  
where \(N_{stitch\_line}\) indicates the number of stitching line pixels. Obviously, the values of these indicators are low for a visual friendly image. Table 1 shows the indicators of Figure 3a,b,d. We process images by the Gaussian smoothing filter with different levels (\(\sigma\)) at the beginning of edge detection. According to Figure 3 and Table 1, our approach performs much better than the traditional methods in the both two situations. The significant decreases of \(N\) when enlarge the value of \(\sigma\) imply that all of three images have many edge pixels that are hard to notice. Nevertheless, there are still about 130 conspicuous edge pixels left in Figure 3a,b with the image size of \(699\times 459\) pixels, while the number in Figure 3d is 73 with a 50% decline compared with Figure 3a. These conspicuous edge pixels in the final image are only 4.58% of the stitching line pixels, compared with the 15.14% and 12.6% in (a) and (b) respectively.