# Three-Dimensional Film Image Classification Using an Optimal Width of Histogram
## 4 Experimental Results and Discussion
Table 2 shows the results of comparison methods of the EPI for good pattern film images and bad pattern film images of Figure 8. In Table 2, all methods except for image subtraction produced values over 1 for the good pattern images, with morphological geodesic active contour showing the highest value. This indicates that image subtraction does not properly detect the edges of the image, and that morphological geodesic active contour detects edges the best. In addition, Canny edge detection and Canny edge detection with HSV produced relatively small values compared to Otsu thresholding, indicating that they detected too many edges. For the bad pattern images, all methods except for image subtraction produced values over 1, with morphological geodesic active contour showing the highest value. On the other hand, Canny edge detection, Canny edge detection with HSV, and Otsu thresholding produced similar values. However, as seen in the results of Figure 8i-k, the methods failed to detect the bad pattern images properly, but all EPI values were over 1. This is because the original images also had very blurry contours, making it difficult to detect the edges. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Specific Height**} & **1/10** & **2/10** & **3/10** & **4/10** & **5/10** & **6/10** & **7/10** & **8/10** & **9/10** \\ \hline Good & Min & 85 & 63 & 29 & 23 & 20 & 12 & 9 & 2 & 1 \\ \cline{2-10} Pattern & Max & 241 & 240 & 229 & 195 & 190 & 158 & 128 & 99 & 89 \\ \hline Bad & Min & 43 & 22 & 17 & 12 & 9 & 6 & 5 & 3 & 1 \\ \cline{2-10} Pattern & Max & 133 & 106 & 86 & 70 & 66 & 58 & 55 & 53 & 40 \\ \hline Threshold & 120 & 95 & 65 & 55 & 35 & 30 & 20 & 15 & 5 \\ \hline Accuracy (\%) & 99.23 & 99.27 & 99.34 & 99.13 & 99.09 & 98.78 & 97.55 & 73.42 & 82.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracy according to the height of the image histogram. The structural similarity index (SSIM) [29] was used to evaluate the similarity between the two images. SSIM is a measure that evaluates similarity using brightness, contrast, and \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Good Pattern Film Images**} & \multicolumn{4}{c}{**Bad Pattern Film Images**} \\ \cline{2-9}  & **Img1** & **Img2** & **Img3** & **Img4** & **Img1** & **Img2** & **Img3** & **Img4** \\ \hline Image subtraction & 0.5445 & 0.4736 & 0.5171 & 0.5040 & 0.2477 & 0.2735 & 0.2372 & 0.2694 \\ Canny edge detection & 3.9533 & 3.9257 & 4.6248 & 3.9869 & 2.0604 & 2.1220 & 1.9288 & 2.0198 \\ Canny edge detection & 3.6100 & 3.5531 & 3.8108 & 3.6666 & 1.8198 & 2.0888 & 1.8439 & 1.9961 \\ with HSV [17] & 4.3954 & 4.3134 & 4.6248 & 4.4331 & 2.0672 & 2.1133 & 1.9076 & 1.9919 \\ Otsu thresholding & 13.4196 & 13.1801 & 14.1203 & 13.5010 & 6.2879 & 6.4296 & 5.8185 & 6.0841 \\ active contour [20] & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of Edge Projection Index for Good pattern film images and Bad pattern film images. Figure 8: Good and bad 3D film images with comparison algorithms. structure of information of two images. The equation for comparing the structure of the pixels constituting the image is as follows:  
\[ \begin{split} SSIM(x,y)&=B(x,y)\cdot C(x,y)\cdot R(x,y) \\ &=\frac{4\mu_{x}\mu_{y}\sigma_{xy}}{\left(\mu_{x}^{2}+\mu_{y}^{2} \right)\left(\sigma_{x}^{2}+\sigma_{y}^{2}\right)}\;,\\ \text{where }B(x,y)&=\frac{2\mu_{x}\mu_{y}}{\mu_{x}^{2} +\mu_{y}^{2}},\;\;C(x,y)=\frac{2\sigma_{x}\sigma_{y}}{\sigma_{x}^{2}+\sigma_{ y}^{2}},\\ R(x,y)&=\frac{2\sigma_{xy}}{\sigma_{x}\sigma_{y}} \end{split}  \tag{10}\]  
where \(B(x,y)\), \(C(x,y)\), and \(R(x,y)\) represent the average brightness, contrast, and correlation of the two images, respectively.