Although Image Processing and Computer Vision are different concepts, they work together in many areas. Computer vision systems aim to replicate the complexity of the human visual system in order to make sense of the digital world. To do this, it can use image processing algorithms within a Deep Learning Neural Network. As a result, it aims to extract meaningful information from these inputs.
Image processing detects patterns in both digital and optical images. Computer vision focuses on better understanding the visual world through the ability of a data-trained artificial intelligence model to analyze photos, videos, and other visual inputs to extract meaningful information and make high-fidelity predictions.
The most common uses of image processing
Pattern recognition involves video processing, image enhancement, and filtering. Computer vision has a wider range of applications and is widely used in areas such as defect detection and object detection.
Image Processing requires costly equipment. Computer Vision has lower equipment costs. For some projects, even an industrial mini PC with a GPU, a webcam, or an IP camera is sufficient. With the same system, dozens of different AI models can be run and different objects can be defined. It saves both time and money.
In some Computer Vision projects, AI can make accurate predictions even on untrained objects. If trained correctly, it can detect even if the conditions and environment change.