Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”
History of computer vision
Early experiments in computer vision took place in the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles and squares. In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. This advancement was used to interpret written text for the blind.
As the internet matured in the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. These growing data sets helped make it possible for machines to identify specific people in photos and videos.
Today, a number of factors have converged to bring about a renaissance in computer vision:
Mobile technology with built-in cameras has saturated the world with photos and videos.
Computing power has become more affordable and easily accessible.
Hardware designed for computer vision and analysis is more widely available.
New algorithms like convolutional neural networks can take advantage of the hardware and software capabilities.
The effects of these advances on the computer vision field have been astounding. Accuracy rates for object identification and classification have gone from 50 percent to 99 percent in less than a decade — and today’s systems are more accurate than humans at quickly detecting and reacting to visual inputs.
Computer vision resembles a jigsaw puzzle
Computers assemble visual images in the same way you might put together a jigsaw puzzle.
Think about how you approach a jigsaw puzzle. You have all these pieces, and you need to assemble them into an image. That’s how neural networks for computer vision work. They distinguish many different pieces of the image, they identify the edges and then model the subcomponents. Using filtering and a series of actions through deep network layers, they can piece all the parts of the image together, much like you would with a puzzle.
The computer isn’t given a final image on the top of a puzzle box — but is often fed hundreds or thousands of related images to train it to recognize specific objects.
Instead of training computers to look for whiskers, tails and pointy ears to recognize a cat, programmers upload millions of photos of cats, and then the model learns on its own the different features that make up a cat.
Computer vision in today’s world
From recognizing faces to processing the live action of a football game, computer vision rivals and surpasses human visual abilities in many areas.
Deep learning and computer vision
How does deep learning train a computer to see? Learn how the different types of neural networks work and how they are used for computer vision.
Image Analysis and AI
See and introduction to image analysis and learn analytical techniques you can apply to image data.
Face recognition demo
Learn the underlying techniques and data processing steps needed for facial recognition and computer vision. This demo shows how a SAS® Viya® model detects, aligns, represents and classifies facial images.
Who's using computer vision?
Computer vision is used across industries to enhance the consumer experience, reduce costs and increase security.
Retailers can use computer vision to enhance the shopping experience, increase loss prevention and detect out-of-stock shelves. Computer vision is already helping customers checkout more quickly – aiding using self-checkout machines or combining with machine learning to alleviate the checkout process completely.
In manufacturing, businesses use computer vision to identify product defects in real time. As the products are coming off the production line, a computer processes images or videos, and flags dozens of different types of defects — even on the smallest of products.
Public Sector agencies use computer vision to better understand the physical condition of assets under their control, including equipment and infrastructure. Computer vision can help agencies perform predictive maintenance by analyzing equipment and infrastructure images to make better decisions on which of these require maintenance. In addition, Public Sector agencies use computer vision to help monitor compliance with policies and regulations. For example, computer vision can be used to detect contraband in cargo, flag potential safety violations in buildings, review labels for adherence to guidelines, and ensure compliance with conservation regulations. Finally, as drones become used more defense and homeland security needs, the use of analytics to identify and analyze critical elements from the visual feed will rise to the forefront of computer vision use cases in the public sector.
In the medical field, computer vision systems thoroughly examine imagery from MRIs, CAT scans and X-rays to detect abnormalities as accurately as human doctors. Medical professionals also use neural networks on three-dimensional images like ultrasounds to detect visual differences in heartbeats and more.
Defense and Security
In high-security environments like banking and casinos, businesses use computer vision for more accurate identification of customers when large amounts of money are being exchanged. It’s impossible for security guards to analyze hundreds of video feeds at once, but a computer vision algorithm can.
In the insurance industry, companies use computer vision to conduct more consistent and accurate vehicle damage assessments. The advancement is reducing fraud and streamlining the claims process.
Computer vision is one of the most remarkable things to come out of the deep learning and artificial intelligence world. The advancements that deep learning has contributed to the computer vision field have really set this field apart. Wayne Thompson SAS Data Scientist
Computer vision for animal conservation
Learn how a computer vision model designed to analyze animal tracks works. Can the computer be trained to see a footprint much like a native animal tracker would? See how the computer processes the different layers of information to determine the animal and its sex. In this video, Jared Peterson, Senior Manager of SAS Advanced Analytics R&D, shows how neural networks are the science behind computer vision.
Seeing results with computer vision
Computer vision users in many industries are seeing real results – and we’ve documented many of them in this infographic. For example, did you know:
- Computer vision can distinguish between staged and real auto damage?
- Computer vision enables facial recognition for security applications?
- Computer vision makes automatic checkout possible in modern retail stores.
From spotting defects in manufacturing to detecting early signs of plant disease in agriculture, computer vision is being used in more areas than you might expect.
Click on the infographic here to see results from retail, banking, health care and more.
How computer vision works
Computer vision works in three basic steps:
Acquiring an image
Images, even large sets, can be acquired in real-time through video, photos or 3D technology for analysis.
Processing the image
Deep learning models automate much of this process, but the models are often trained by first being fed thousands of labeled or pre-identified images.
Understanding the image
The final step is the interpretative step, where an object is identified or classified.
Today’s AI systems can go a step further and take actions based on an understanding of the image. There are many types of computer vision that are used in different ways:
- Image segmentation partitions an image into multiple regions or pieces to be examined separately.
- Object detection identifies a specific object in an image. Advanced object detection recognizes many objects in a single image: a football field, an offensive player, a defensive player, a ball and so on. These models use an X,Y coordinate to create a bounding box and identify everything inside the box.
- Facial recognition is an advanced type of object detection that not only recognizes a human face in an image, but identifies a specific individual.
- Edge detection is a technique used to identify the outside edge of an object or landscape to better identify what is in the image.
- Pattern detection is a process of recognizing repeated shapes, colors and other visual indicators in images.
- Image classification groups images into different categories.
- Feature matching is a type of pattern detection that matches similarities in images to help classify them.
Simple applications of computer vision may only use one of these techniques, but more advanced uses, like computer vision for self-driving cars, rely on multiple techniques to accomplish their goal.
Seeing is believing. Discover what SAS can help you achieve with computer vision.
Featured product for Computer Vision
SAS® Visual Data Mining and Machine Learning
This SAS solution supports clustering, different flavors of regression, random forests, gradient boosting models, support vector machines, sentiment analysis and more, in addition to deep learning. An interactive, visual pipeline environment presents each project (or goal) as a series of color-coded steps that occur in a logical sequence.
- Article Three steps for conquering the last mile of analyticsPutting your analytical models into production can be the most difficult part of the analytics journey. It’s no surprise that this last mile of analytics – bringing models into deployment – is the hardest part of digital transformation initiatives for organizations to master, yet it’s the most crucial.
- Article AI in manufacturing: New opportunities for IT and operationsAn AI survey reveals that leaders and early adopters in AI are making important advances and are identifying and expanding on what works as they use AI in more ways and more parts of their organizations.
- Article How to improve your AI marketing skillsMarketing teams can use current AI capabilities to enhance their efforts around campaign automation, dynamic pricing based on forecasting models, and by providing more relevant, real-time customer offers.