###### Abstract
As a significant difference in this research, the vision system technique is used to present data in matrix form; the values are analyzed as the intensities of the pixels in an image and are made the segmentation of the matrix based on the control limits, the base of the statistical control of the process, generating visual patterns, the main features of the visual pattern are extracted using Hu moments theory. Finally, the classification is made intelligently using the k-means algorithm, presenting a novel alternative for analyzing sampling data in process control. A computer vision algorithm for defect detection involves two main steps. The first consists of a precise segmentation of objects from the background, and the second involves an accurate identification of defects through the extraction and comparison of physical characteristics of objects. Computer vision algorithms are a more effective option for sorting and selecting products when parsing and extracting useful information from a specific object present in an image [21]. Vision systems use feature extraction based on invariant Hu moments for the recognition or classification of objects in images. These systems have been successfully implemented in various sectors. For instance, [PERSON] [22] addressed the recognition of microscale concave, convex, and flat surfaces via Hu moments patterns based on micro-laser line contouring. Thus, the pattern of Hu moments recognizes the flat, convex, and concave surfaces of each. Similarly, the classification of objects was carried out using the similarity of the contour with the Hu moments. The vision system aims to detect, classify, and manipulate objects within the robot's workspace using only 2D camera images [23]. The authors of [24] integrated Hu moments into breast tumor analysis to aid in the clinical diagnosis of breast cancer, providing a good combination of segmentation and Hu moments theory. The Hu invariant moments are fed to a k-nearest neighbor classifier (k-NN) and a radial function-based neural network (RBFNN) to classify breast tumors as benign or malignant. [PERSON] et al. (2019) proposed an assembly inspection method based on artificial vision and a deep neural network. With the results of image segmentation, the area, perimeter, circularity, and Hu invariant moment are extracted from the contour to form the vector of characteristics. The imaging platform is built to collect images of parts and assemblies. They utilized [PERSON]'s R-CNN model to identify and segment the shape of each part of the image, obtaining the category coordinates and position of the part within the image. Therefore, the proposed method applies translation invariance and rotation invariance to the characteristic of Hu moments to extract shape information from the same coherent area of the image. The texton template is applied to detect and extract the consistent area of an image, and it calculates the dominant color descriptor feature on pixels within this coherent area ([PERSON], 2016). The authors of ([PERSON], 2016) aim at hand gesture recognition. Here, modified Hu moments are proposed on the platform of a Kinect sensor and a fingertip's gradient orientation using the finger Fourier descriptor (FGFF). Two algorithms are designed to extract the characteristics emphasized by the fingertip and its gradient orientations. Then, the momentum invariants of Hu modified to encode the structure accentuated by the contour in the region of the hand with much lower exponents are discussed. The seven invariant moments are calculated for the rotated version of the test image. Proven results are more competitive and promising and are validated by orthopedic surgeons and rheumatologists. Focused on early detection and gradation of osteoarthritis of the knee using the invariant moments of Hu to understand the transformation of the cartilage region into knee X-ray images ([PERSON], 2016).