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“Computer Vision” refers to the use of computers to achieve human visual functions, perceiving, recognizing, and understanding three-dimensional scenes of the objective world. Computer vision is a cutting-edge field. We believe that computer vision, or simply “vision,” is a discipline distinct from the study of human or animal vision. It constructs models using geometry, physics, and learning techniques, thereby employing statistical methods to process data. Therefore, from our perspective, based on a thorough understanding of camera performance and the physical imaging process, vision performs simple reasoning on each pixel, synthesizing potential information from multiple images into a harmonious whole, determining the relationships between pixel sets to segregate them or infer certain shape information, using geometric information or probabilistic techniques to identify objects.
“Machine Vision” refers to using machines to replace human eyes for measurement and judgment. A machine vision system captures images through machine vision products (which include image acquisition devices, either CMOS or CCD), then transmits the image to a processing unit. Through digital processing, it makes determinations regarding size, shape, color, etc., based on pixel distribution and brightness, color, and other information. Subsequently, based on the determination results, it controls the actions of on-site equipment. Currently, it is widely used in industries such as food and beverage, cosmetics, building materials and chemicals, metal processing, electronics manufacturing, packaging, and automotive manufacturing.
Machine vision is a relatively new technology that provides many techniques for the manufacturing industry to improve product quality, production efficiency, and operational safety. Among other related technologies, machine vision includes image digitization, image manipulation, and image analysis, typically using computers to achieve these tasks. Therefore, it is a field that encompasses both image processing and computer vision. However, we emphasize that machine vision, computer vision, and image processing are not synonymous. None of them is a subset of the other two. Computer vision is a branch of computer science, while machine vision is a specialized area of systems engineering. Machine vision does not specify the use of computers, but often utilizes specialized image processing hardware to achieve high-speed processing that ordinary computers cannot reach.
Both are essentially forms of vision and are interdisciplinary. Computer vision focuses a bit more on computers and processing, while machine vision is more about machines and the selection of cameras. Computer vision is more academic, with many papers in CVPR, while machine vision is more engineering-oriented, with more applications in the automation industry. To metaphorically illustrate, pointing a camera at a person represents computer vision (indicating a close relationship with artificial intelligence). Pointing a camera at a machine (in a workshop) represents machine vision.
Machine vision is an application of computer vision in factory automation. Just as a supervisor works on an assembly line, visually monitoring objects and judging their quality, a machine vision system uses cameras and image processing software to complete similar monitoring tasks. A machine vision system is a computer that makes decisions based on digital image analysis.
In summary, there is no clear boundary between machine vision and computer vision; they are closely linked, sharing the same theories, but differing in practical applications. Both computer vision and machine vision aim to extract information from images or sequences of images.
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Download 1: OpenCV-Contrib Extension Module Chinese Tutorial
Reply "Extension Module Chinese Tutorial" in the backend of the "Beginner Learning Vision" public account to download the first Chinese version of the OpenCV extension module tutorial available online, covering installation, SFM algorithms, stereo vision, object tracking, biological vision, super-resolution processing, and more than twenty chapters of content.
Download 2: 52 Python Vision Practical Projects
Reply "Python Vision Practical Projects" in the backend of the "Beginner Learning Vision" public account to download 31 practical vision projects including image segmentation, mask detection, lane line detection, vehicle counting, eyeliner addition, license plate recognition, character recognition, emotion detection, text content extraction, and facial recognition, to aid in rapidly learning computer vision.
Download 3: 20 OpenCV Practical Projects
Reply "20 OpenCV Practical Projects" in the backend of the "Beginner Learning Vision" public account to download 20 practical projects based on OpenCV for advanced learning.
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