Moreover, the tedious work is often intolerable with such lighting, air and noise in the coal preparation workshop. Thus, technology for the automatic separation of coal and gangue is severely needed in the coal mining industry. Currently, the automated separation is performed based on the physical or chemical properties of the various components of raw coal. In view of water use, the methods of coal and gangue separation can be divided into the dry technique or the wet technique [1]. For example, the moving-sieve jigging method and the heavy-medium separation method are the most common wet techniques. Their fundamental principles are very simple, i.e., the different dynamic behaviors of coal and gangue in water. Nonetheless, the enormous maintenance cost and usage of water resources make the wet technique unsuitable for arid regions. Dry techniques apply water-free devices with \(\gamma\)-rays or X-rays in the existing industrial applications. However, the problems of poor recognition rate and the cumbersome management of radiation sources remain unsolved [3]. Another promising method in recent years to separate coal and gangue is the automated coal-gangue separation system with computer vision, in which industry cameras and computers are used to identify whether the object is coal or gangue through the pattern recognition technology. [PERSON] et al. proposed the coal gangue on-line recognition and automation selection system with digital image processing technology [4]. [PERSON] et al. studied the pneumatic separation of coal and gangue based on the machine vision system [5]. Figure 2 shows a design of an automated coal-gangue separation system by using the computer vision technology [5; 6]. It is worth noting that the computer vision is very similar to the conventional manual separation process of coal and gangue with the development of machine learning (ML) and artificial intelligence (AI). The separation of coal and gangue based on the computer vision method can be divided into the following steps [5]:  
1. Read digital image obtained from the camera by computers. 2. Determine the identification characteristics of coal and gangue. 3. Find gangue materials based on the identification algorithms. 4. Determine the size and the location of gangue, and then convert these information to the control signals of high pressure air nozzle. Figure 2 shows a design of an automated coal-gangue separation system by using the computer vision technology [5; 6]. Figure 1: Manual separation of coal and gangue. In addition, it is interesting to note the feasibility of implementation of image recognition techniques since we could directly modify the current devices from dry techniques by replacing the \(\gamma\)-rays or X-rays with industrial cameras. Therefore, the use of image recognition to replace the manual separation of coal and gangue has the bright future with advantages of the real-time processing, higher intelligence and lower cost. However, the recognition rate of the method with computer vision is not satisfactory. Meanwhile, the large computation of the pixel matrices of images brings it low processing speed and poor real-time capability. The challenging task of the feature extraction and classification recognition is looking for the most efficient variables in a great deal of features, which should be distinguishable, reliable, independent and fast. At present, most research is on the grayscale and texture features of coal and gangue via the image recognition method, while the geometric features of their outline curves have not been studied adequately. In reality, because of the distinct hardness of coal and gangue, the edge profiles should be different after the long transportation from underground mining sites. Fracturing in rocks at all scales, from the microscale (microcracks) to the continental scale (megafaults), leads to fractal structures [7].