# Three-Dimensional Film Image Classification Using an Optimal Width of Histogram
## 2 Related Methods  
In order to classify 3D film images as good or bad, a particular image or the characteristics of the images should be detected. Methods for detecting or classifying a particular object in an image include image subtraction, segmentation, SVM, and template matching. Image subtraction is used to extract a particular object in an image using differences between images or to find different parts between images, and studies using image subtraction have been recently published in the astronomical, astrophysical, and medical areas [3; 4; 5; 6; 7]. In particular, [PERSON] et al. proposed the saccadic fast Fourier transform (SFFT) algorithm for image subtraction in time-domain astronomy, which uses a \(\delta\)-function basis for kernel decomposition and performs the subtraction in Fourier space [4]. [PERSON] et al. proposed an unsupervised approach using a cycle-GAN for efficient and accurate segmentation of pneumonia lesions in CT scans [5]. In this approach, they performed lung volume segmentation, healthy lung image synthesis, infected and healthy image subtraction, and binary lesion mask generation. Binarization and edge detection are representative rule-based methods for segmentation. Binarization is a method of changing the pixel to black or white when it is high or low based on a certain threshold, and generally includes Global thresholding, adaptive thresholding, Otsu thresholding, etc. [8; 9; 10; 11]. Adaptive thresholding is a method of applying different thresholds to each area, and Otsu thresholding is an algorithm that binarizes pixels in an image by finding thresholds that minimize variance or maximize differences between classes when classified into two classes. Recently, [PERSON] et al. proposed an algorithm for road crack detection in images using deep learning and adaptive thresholding [10]. The algorithm extracted cracks from the road surface with an adaptive thresholding method, resulting in high accuracy in image classification and successful extraction of cracks. [PERSON] proposed an intelligent model based on image processing techniques for automatic crack recognition and analyses [11]. The model used the Otsu method for thresholding, followed by a gray intensity adjustment method called M2 GLD, to improve the accuracy of crack detection results. Edge detection methods detect the edge of objects in an image by looking for a point where the brightness of the image changes suddenly through a differential equation, and they include Sobel edge detection, Canny edge detection, Laplacian edge detection, etc. [12; 13; 14]. Sobel edge detection is more resistant to noise than other edge detection algorithms and reacts more sensitively to diagonal edges than to vertical and horizontal components. Canny edge detection is an algorithm aimed at detecting strong edges that are not sensitive to noise. Unlike other edge detection algorithms, Laplacian edge detection uses a second-order differential equation. It is famous for edge detection because it detects the edges in a bright or dark area. [PERSON] et al. have proposed an improvement to the traditional Canny edge operator for static gesture segmentation by using a combined filtering method and adaptive threshold algorithm, resulting in high accuracy in simple static gesture segmentation [15]. [PERSON] et al. have proposed an effective scheme for detecting blur in digital images based on edge analysis using the Laplacian operator, which can determine the extent of blur through the variance of the Laplacian method [16]. Additionally, the paper presents a simple and fast algorithm for estimating image noise using an easy edge detection algorithm and Laplacian operator, which performs well for different types of images over a large range of noise variances. Recently, edge detection research by Canny edge detection after applying HSV color in [17], and a geodesic active contour algorithm that often detects gradually developing boundaries in [18], have been published.