Today's computer vision technology uses deep learning algorithms, specifically convolutional neural networks (CNN), to make sense of images. These networks are trained on thousands of sample images to help the algorithm understand and break down the content of an image. Various computer vision technologies, including facial recognition for personal login and moving object detection for video surveillance, have been developed and are now mature enough to be used in our daily lives. The accuracy of image segmentation, which detects regions of interest or structural features of an object, is critical for automatic pattern recognition in food image analysis. Before analyzing digital images, they must be pre-processed to improve quality by removing noise, enhancing contrast, and converting to grayscale. The facial recognition market is expected to grow and has a variety of commercial applications, but concerns about data privacy persist. As computer vision technology is refined and improved, it will be able to perform more functions and discern more from images. It can be combined with other technologies or AI subsets to create more powerful applications, including helping visually challenged individuals through image captioning and natural language generation. Computer vision will also play a significant role in the development of artificial superintelligence.