# Three-Dimensional Film Image Classification Using an Optimal Width of Histogram
## 4 Experimental Results and Discussion  
In order to evaluate the performance of the proposed algorithm, the experiment was conducted using a total of 2580 3D film images, of which 2136 are good 3D pattern film images, and 714 are bad 3D pattern film images. The evaluation was performed by cropping all patterns individually into a size of 169 \(\times\) 169, as shown in Figure 6. The experiments of this work were performed on Windows 10 Pro and Python 3.6. Figure 7a shows an image histogram for good 3D film images, and (b) shows an image histogram for bad 3D film images. As shown in Figure 7a, the width of the pixel value on the x-axis is wide because the contrast of the pixel in the image was high, but (b) shows that the width of the pixel is narrow because the contrast of the pixel in the image was low. Based on these  
Figure 4: The \(H(\alpha)_{min}\) and \(H(\alpha)_{max}\) at specific height \(\alpha\) in the image histogram. Figure 5: Procedures of the proposed algorithm. characteristics of good and bad 3D film images, the results of obtaining the width of the height from 1/10 to 9/10 for the image histogram for each image are shown in Table 1. As shown in Table 1, when comparing the minimum value and the maximum value of the width of good and bad 3D film images, it can be observed that the lower the height of the image histogram, the larger the difference in width between good and bad 3D film images. As a result of determining the threshold for each height of the image histogram, the classification accuracy was found to be over 99% from 1/10 to 5/10 of the image histogram height, and in particular, the classification accuracy was the highest at 99.34% at 3/10 of the image histogram height. Figure 6: Separated good and bad 3D film images. Figure 7: Good and bad 3D film images with image histogram. To evaluate the performance of the proposed algorithm, the analysis results were compared using image subtraction, Canny edge detection, Canny edge detection with HSV [10], Otsu thresholding, morphological geodesic active contour [13], and SVM with Canny edge detection [16]. Figure 8a-l show good and bad 3D film images originally and with image subtraction, Canny edge detection, Canny edge with HSV, Otsu thresholding, and morphological geodesic active contour, respectively. Figure 8b shows the result of applying the image subtraction of the control good 3D film image to good 3D film images, and (h) shows the image subtraction image of the control good 3D film image and bad 3D film images, where black is the difference between the two images. In Canny edge detection, the minimum threshold value and the maximum threshold value were set to 10 and 50, respectively. As shown in Figure 8c,d, a lot of edges in the image were detected when Canny edge detection was applied. On the other hand, Figure 8i,j show that the pattern of the image was hardly detected when Canny edge detection was applied; (k) and (l) show that the pattern was not accurately detected. Therefore, it was found that most of the pattern detection results in the bad 3D film image were almost poor. We used the Edge Preservation Index (EPI) [28] to evaluate the performance of the comparison methods. This metric is used to measure the preservation of image edges before and after processing. The equation is as follows  
\[\text{EPI}=\frac{\sum\lvert P_{s}(i,j)-P_{s}(i-1,j+1)\rvert}{\sum\lvert P_{ij} (i,j)-P_{ij}(i-1,j+1)\rvert}, \tag{9}\]  
where \(P_{s}\) is the value of the image-processed image pixel in \((i,j)\), \(P_{ij}\) is the value of the original image pixel in \((i,j)\); the \(P_{s}(i,j)\) and \(P_{0}(i,j)\) are in the edge area where intensity sharply varies. From the EPI results, a \(s\) higher EPI value indicates that the processed image preserves the edges of the original image. On the contrary, a lower EPI value means that the processed image significantly alters or removes the edges of the original image, resulting in a lower degree of preservation.