{"id":1996,"date":"2026-04-27T05:57:18","date_gmt":"2026-04-27T05:57:18","guid":{"rendered":"https:\/\/novanta.com\/precision-medicine\/?p=1996"},"modified":"2026-06-12T19:54:08","modified_gmt":"2026-06-12T19:54:08","slug":"what-is-machine-vision-how-does-it-work","status":"publish","type":"post","link":"https:\/\/novanta.com\/precision-medicine\/articles\/what-is-machine-vision-how-does-it-work\/","title":{"rendered":"What Is Machine Vision & How Does It Work?"},"content":{"rendered":"\n

Machine Vision: What Is It?<\/h2>\n\n\n\n

Machine vision (MV) enables machines, with the use of computers, to process and interpret visual information like humans do. This helps machines perform tasks that typically require human perception and interpretation. Machine vision involves capturing an image or a series of images, while processing and analyzing the data to extract meaningful information. This technology has numerous applications in various industries These industries include\u00a0manufacturing process automation,\u00a0healthcare engineering,\u00a0supply chain engineering, and security, among others.<\/p>\n\n\n\n

Machine vision systems typically consist of a\u00a0machine vision camera<\/a>\u00a0or sensor, a processor or computer to analyze the images. The system also includes software that provides the necessary algorithms for processing and interpretation. The technology has advanced significantly in recent years, thanks to the increasing availability of powerful computers, high-quality cameras, IoT, cloud computing, and sophisticated algorithms that can process large amounts of data in real-time.<\/p>\n\n\n\n

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Machine Vision: How Does It Work?<\/h2>\n\n\n\n

Machine vision works by using various hardware and software components to capture, process, and analyze visual information. The process for machine vision generally follows these tasks:<\/p>\n\n\n\n

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  1. Image Capture: <\/strong>Captures an image or series of images using a digital camera or sensor. The camera mounts on a robotic arm, conveyor belt, or other equipment to capture images from different angles or positions.<\/li>\n\n\n\n
  2. Preprocessing: <\/strong>Before analyzing the images, data may need preprocessing. This is to correct for distortion, lighting, or other factors that could affect the accuracy of the analysis. Preprocessing involves adjusting brightness and contrast of the image, removing background noise, or applying filters to enhance features of interest.<\/li>\n\n\n\n
  3. Feature Extraction: <\/strong>Once the image preprocesses, machine vision algorithms extract specific features from the image, such as edges, corners, or shapes. This may involve applying edge detection or segmentation algorithms to identify distinct regions within the image.<\/li>\n\n\n\n
  4. Pattern Recognition: <\/strong>After the features extract, machine learning algorithms recognize patterns or objects within the image. This may involve training the algorithm on a dataset of labeled images to learn what different objects look like. And then, using that knowledge to identify similar objects in new images.<\/li>\n\n\n\n
  5. Decision Making: <\/strong>Once the pattern recognition algorithms have identified objects within the image, machine vision systems make decisions based on that information. For example, if a defect is detected in a manufacturing line, the system automatically stops production to prevent further damage.<\/li>\n<\/ol>\n\n\n\n

    Benefits Of Machine Vision<\/h2>\n\n\n\n

    Machine vision has the potential to enhance productivity, accuracy, and efficiency. This is due to machines processing data at a faster rate and with greater precision than humans. It can also help reduce costs by automating repetitive visual inspection tasks and eliminating human errors. <\/p>\n\n\n\n

    Engineering<\/h3>\n\n\n\n

    Machine vision serves the engineering industry in several ways, including:<\/p>\n\n\n\n