The "Industrial Machine Vision Market by Component (Hardware (Camera, Frame Grabber, Optics, Processor), and Software (Deep Learning, and Application Specific)), Product (PC-based, and Smart Camera-based), Application, End-User - Global Forecast to 2023" report has been added to ResearchAndMarkets.com's offering. The overall industrial machine vision market was valued at USD 7.91 Billion in 2017 and is expected to reach USD 12.29 Billion by 2023, at a CAGR of 7.61% between 2017 and 2023. This is because of the increasing need for quality inspection and automation, growing demand for AI and IoT integrated machine vision system, increasing adoption of Industrial 4.0, development of new connected technologies, and government initiatives to support smart factories, among others. Governments of different countries worldwide are encouraging investments in manufacturing, which is necessitating the use of various automation products for structural development. Software component is expected to grow at the highest rate between 2017 and 2023.
Computer vision is the field of study surrounding how computers see and understand digital images and videos. Computer vision spans all tasks performed by biological vision systems, including "seeing" or sensing a visual stimulus, understanding what is being seen, and extracting complex information into a form that can be used in other processes. This interdisciplinary field simulates and automates these elements of human vision systems using sensors, computers, and machine learning algorithms. Computer vision is the theory underlying artificial intelligence systems' ability to see and understand their surrounding environment. There are many examples of computer vision applied because its theory spans any area where a computer will see its surroundings in some form.
Computers are getting better each year at AI-style tasks, especially those involving vision--identifying a face, say, or telling if a picture contains a certain object. In fact, their progress has been so significant that some researchers now believe the standardized tests used to evaluate these programs have become too easy to pass, and therefore need to be made more demanding. At issue are the "public data sets" commonly used by vision researchers to benchmark their progress, such as LabelMe at MIT or Labeled Faces in the Wild at the University of Massachusetts, Amherst. The former, for example, contains photographs that have been labeled via crowdsourcing, so that a photo of street scene might have a "car" and a "tree" and a "pedestrian" highlighted and tagged. Success rates have been climbing for computer vision programs that can find these objects, with most of the credit for that improvement going to machine learning techniques such as convolutional networks, often called Deep Learning.
Computer vision is fundamental for a broad set of Internet of Things (IoT) applications. Household monitoring systems use cameras to provide family members with a view of what's going on at home. Robots and drones use vision processing to map their environment and avoid obstacles in flight. Augmented reality glasses use computer vision to overlay important information on the user's view, and cars stitch images from multiple cameras mounted in the vehicle to provide drivers with a surround or "bird's eye" view which helps prevent collisions.
Build this Raspberry Pi guardian robot and stave off intrusions! A new DIY build, courtesy of the good folks at Dexter Industries. Advances in machine vision may soon allow you to search using images instead of keywords. That may not seem wholly intuitive, but images contain a wealth of information, which makes searching via user-uploaded images a tool with lots of potential. Engineers at Shutterstock, which has a library of 80 million stock photos, have been experimenting with machine vision and machine learning to augment their search capability.