# Three-Dimensional Film Image Classification Using an Optimal Width of Histogram
## 1 Introduction  
The 3D pattern film image is a three-dimensional image with a shadow formed on the opposite side of the incident surface of light, which cannot be confirmed by shape and touch but can only be confirmed by sight. Figure 1 shows 3D film images, of which (a) is a good 3D film image and (b) is a bad 3D film image. Figure 1(a) is a 3D film image printing, and (b) is an inspection machine. The 3D film image is printed in the printing machine, and after that, it is moved to the inspection machine and determined to be a good or bad 3D film image through the image taken with the camera. It is attached to the surface of various products such as cosmetics, liquor bottles, and book covers using good 3D film images classified through quality inspection.  
Figure 1: Two types of 3D pattern film images.  
Figure 3a is a product without 3D film images, and (b) is a product with 3D film images. As shown in Figure 3, it can be used in marketing that stimulates the curiosity of buyers and increases their desire to purchase by attaching 3D film images to the surface of the product. However, the 3D film image is a recently developed technology that has not yet been commercialized, and is in the process of technical research for mass production and automatic quality inspection. In order to show the pattern of the 3D film image in three dimensions, the difference in contrast between pixels in the image should be large, and the contour of the pattern should be clear, as shown in Figure 1a. On the other hand, as shown in Figure 1b, if the difference in contrast between pixels in the image is not large and the contour of the pattern is not clear, it does not look three-dimensional. For this reason, quality inspection for 3D film images is essential. It is difficult to recognize the pattern in the 3D film image when the bad 3D film image is attached to the product. In addition, various methods for evaluating the quality of products produced using 3D printers, such as dimensional accuracy, surface roughness, and part density, have been extensively studied [1,2]. However, there are few methods used to classify good or bad 3D film images since it is a recently developed product. To inspect the 3D film pattern image, we can use various methods that have been used to detect and classify a particular pattern in an image, including image subtraction, binarization, segmentation, support vector machine (SVM), template matching, etc. [3-26]; however, it is not easy to classify good or bad 3D film images using the above-mentioned methods.  
In this paper, we propose a method for classifying good or bad 3D film images based on a threshold using the width corresponding to a specific frequency of image histogram for a 3D film image. Since the method just uses the width of the specific height of the image histogram, the analysis process is simple. Additionally, it is not necessary to detect the pattern in an image because the method analyzes the characteristics of each pattern of good or bad 3D film images using the image histogram, and it can be classified even if the contrast of the pattern is low.  
Figure 3: Commercial good without and with 3D film.  
Figure 2: Three-dimensional film image production and inspection machine.  
The composition of this paper is as follows. Section 2 introduces methods to detect a particular image in the image and classify good 3D film images and bad 3D film images. Section 3 describes the algorithm proposed in this paper, and Section 4 presents the experimental results of the proposed method. Finally, Section 5 concludes this paper.