### Literature Review
The PCB automated inspection algorithm was tested using an actual PCB pattern on a PC/AT with a PCVISION-plus frame grabber. Polarized filters were used both in the lighting device and the camera lens to facilitate the quality of the images [9]. An actual PCB pattern was tested by the proposed method. All the defects were correctly detected. The method is considered efficient for speed performance because only simple features are used to classify the defects. In addition, operations such as subtraction/elimination and counting the numbers of the objects (or backgrounds) in the inspected image and the template can be implemented by parallel processing, reducing the processing time substantially. Further, the PCB alignment problem can be solved by using an external mechanical fixture to aid positioning. Overall, the proposed automated PCB inspection method, independent of the size and composition of the inspected PCB pattern, is straightforward and effective [9]. In [10], the authors described production monitoring and control of the biscuit quality where the authors were dealing with the testing of the commercial biscuits by the machine vision system. Automation of visual quality control of the cookies and biscuits is essential for the companies that produce them. The financial matters are significantly reduced, higher accuracy is secured, and quality control is at a higher level. Physical properties, quality, and failures in baking the cookies are possible to detect by machine vision systems. The metal ball surface error detection device was introduced by [11]. The proposed system can inspect the entire surface of a ball by capturing multiple greyscale images with two advanced CCD cameras as the ball rolls on an inclined rail. The specular reflectance of the metal surface is lessened by installing a shade around the ball. Defects are detected by comparing each captured image with its corresponding reference image. Unlike the case of a flat metal surface, the reflectance by the spherical shape of a ball is nonuniform, which exacerbates the difficulty. The use of well-arranged illumination can effectively tackle this problem. For example, ring light illumination is used, and images are taken at varying distances between the light source and a metal ball. Any ball surface defect can be easily detected from a corresponding anomaly in the ring light image. One problem most existing systems possess is that it is difficult to satisfy the industry's high-speed processing because some parts of the measurement systems usually need to be moved around a stationary ball for inspection [11]. Edge detection is a fundamental tool in image processing, machine vision, and computer vision, particularly in feature detection and feature extraction. It is also used in our case, and it is one of the crucial parts of object recognition, as can be seen in this paper. The authors of [12] studied and detected the edges using the vast difference in Gaussian filters applied on many different images with different sizes, and then measured the quality of images using the PSNR, MSE, and NAE. Edge detection is the first stage in recouping data from images. Because of its importance, edge detection is a dynamic research region, producing a set of continuous curves that indicate the boundaries of objects when an edge detector is applied to a group of images. This situation is called ideal surface boundaries and curves corresponding to surface orientation interruptions. Data may be significantly reduced when using an edge detection algorithm on an image because it purifies important information and ignores less critical data. Still, it preserves the structural characteristics of the picture. Clarification of the original image's information contents may be simplified if the edge detection step is successful [12]. The author in [13] spoke about the industrial application of an automated inspection system that aims to enhance the efficiency and flexibility of a computer-integrated manufacturing system (CIMS).