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Computer vision is a subfield of artificial intelligence (AI) that trains computer software on understanding and extracting information from images and video data.

Computer vision seeks to imitate and automate the human visual system. The technology can be used in facial recognition, image matching, and visual object identification.

See below to learn all about the global computer vision market:

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The computer vision market was valued at $12.2 billion in 2020. Expected to maintain a compound annual growth rate (CAGR) of 6.4% over the forecast period from 2020 to 2027, its expected to reach $18.9 billion by the end of it.

Regionally, the global computer vision market is forecast to grow as follows:

By vertical, the industrial segment accounted for 51% of the global computer vision market revenue in 2020, covering industries from automotive and consumer electronics to packaging and machinery.

Other notable industries include:

Computer vision technology application in business is still relatively low. However, a 2021 IDG/Insight survey found that while only 10% of organizations are currently using computer vision, 81% are in the process of investigating or implementing the technology.

Participants in the survey from various industries are looking to use computer vision to improve organization security and employee safety conditions.

Computer vision is starting to change society and the whole world as it becomes ubiquitous. Autonomous vehicles and other industries rely on this technology to increase human capacity, says Abhinai Srivastava, member of the Forbes Technology Council.

Reaching the full potential of computer vision will be possible once we can transition from research labs into the real world.

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Computer vision combines the capabilities of AI and deep learning, forming neural networks that enable computers to process and analyze image and video data.

Systems can be trained using different models for various purposes, from specific object detection to image classification to facial recognition.

Computer vision techniques include:

Object detection is responsible for finding and identifying objects in imaging. Using deep learning and machine learning algorithms, this type of computer vision can detect and identify the characteristics of objects in various forms.

Object detection is most commonly used in manufacturing, warehousing, and stocking. A single, high-quality image of numerous objects can be broken down in the quantity and type of objects.

Object Tracking techniques are capable of detecting multiple objects in a video. Object Tracking computer vision algorithms can be trained to detect and track a specific subset of objects, such as faces, pedestrians, or a species of animal.

While unable to differentiate between the objects it detects, object tracking can be used in self-driving cars and navigation technology.

Instead of focusing on parts of an image, image classification is concerned with labeling an image in its entirety.

When looking for a specific element of a picture, imagine classification can be used in medical imaging, traffic control, and search engines.

Semantic segmentation attempts to understand an image beyond its main components. By dividing the image into groups of pixels, the computer vision model can identify objects within an image, as well as the differences between them.

While object detection is only able to give the approximate location of an object, semantic segmentation takes things a step further by finding the objects boundaries in the image, and as a result, its specific location.

Instance segmentation is able to identify every object instance for every object within an image or video. Its able to detect and mask the object in question, one pixel at a time.

Advanced instance segmentation models can handle overlapping objects and background elements. By identifying the objects and setting their boundaries, instance segmentation ensures the size and distance of an object are more accurate.

As a field of AI, computer vision is another technique meant to make devices, software, and machines smarter and more autonomous.

Different levels of computer vision, specializing in different subfields of vision offer various benefits in their applications, such as:

The developments with computer vision in recent years were facilitated by machine learning technology in particular, the iterative learning process of neural networks and significant leaps in computing power, data storage, and high-quality yet inexpensive input devices, says Bernard Marr, author, and strategic business and technology advisor.

There are endless applications where the ability to extract meaning from seeing visual data is useful. Computer vision combines with other technologies, such as augmented and virtual realities to enable additional capabilities.

Thanks to its countless applications and capabilities, computer vision technology is used by companies and organizations in various industries.

Solera Holdings is a provider of data, applications, and financial services for the automotive and insurance industries. Founded in 2005, Solera now manages over 300 million financial transactions annually with a team of 6,500 professionals worldwide.

Solera Holdings carries a massive database of damage claims in images and videos that require careful processing for settlements and payments.

Using Google Cloud AI/ML products, Solera launched Qapter in 2020, an intelligent solution designed for the entirety of the vehicle claims cycle.

Insurance companies had encountered a number of challenges in trying to commercialize computer vision solutions. They would do their research projects, and could usually build a working solution in-house, but they couldnt scale. What we learned from this is the importance of building a productized solution to avoid failing as an AI project, says Marcos Malzone, Vice President of Product Management at Solera Holdings.

Using visual data from insurance claims, Solera Holdings was able to offer a faster and more accurate cost estimation for the drivers, insurance providers, and automotive technicians.

Amsterdam University Medical Centers (UMC) is one of the leading international centers in academic medicine in the Netherlands. Based in a University, Amsterdam UMC is responsible for providing treatments for its patients, academic medical research, and providing medical education for enrolled students.

Home to one of Europes largest oncology centers, Amsterdam UMC regularly collects massive amounts of data on its patients, ranging from standard patient records to biomarkers, DNA, and genomic data.

Working with SAS, Amsterdam UMC was able to employ computer vision and predictive analytics in order to identify cancer patients. The AI model provides researchers and physicians with a 3D representation of each tumor and its volume once detected.

Were now capable of fully automating the response evaluation, and thats really big news. The process is not only faster but more accurate than when its conducted by humans, says Dr. Geert Kazemier, Professor of Surgery and Director of Surgical Oncology at Amsterdam UMC.

There are a lot of people working with the SAS platform who do not have analytic or data science training. This is the next phase of analytics for us, and I see tremendous opportunities ahead, adds Dr. Kazemier.

Thanks to SAS computer vision and analytics, the researchers at Amsterdam UMC were able to obtain test and research results faster, and detect various forms of cancer in early stages in patients with more research to come.

TripleLift is a programmatic advertising technology company that develops complete advertising campaigns for clients in a wide variety of industries. Founded in 2012 in New York, it provides 13 formats of TV, video, and branded content advertising material.

As media consumers demanded shorter and fewer ads, TripleLift used machine learning to composite non intrusive brand ads onto select scenes of TV and streaming shows. It uses computer vision to analyze video content to determine the moment and location of ad insertion.

AWS solutions can do the work in about half the time it would take a human to do so manually. Now our creative team has more time to do creative work, not just watch videos, says Luis Bracamontes, computer vision and ML engineer at TripleLift.

As we receive more volume, the solution generates insertions faster. So it both saves time and scales to help us manage high volumes of content, adds Bracamontes.

Using Amazon Rekognition and Amazon SageMakeralong with other AWS solutionsTripleLift was able to build a video analysis infrastructure in less than 6 months and reduce video analysis time by 50%.

Some of the leading players in the global computer vision market include:

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Read more here:
The Computer Vision Market in 2022 - Datamation