With the rise of artificial intelligence, algorithms are improving visual tasks. Today’s computer vision applications can already read text with ease. They can identify objects, classify them and track their movement. They can recognize human faces and transform them convincingly. In addition, computer vision enables machines to understand and interpret visual data. From medical imaging to fraud detection to autonomous driving, this technology is poised to revolutionize almost every industry.
As a result, several companies, both digital natives and on business premises, are increasingly using computer vision programs in their operations or exploring new applications for this technology.
Computer vision is not just about building systems that see, but building systems that can interpret what they see.
In this article we define computer vision and explore its growth and operation.
Computer vision definition
First, how do we define computer vision? Let’s start with the basics. To simplify, computer vision AI is the field of computer science that enables computer systems to see and understand the world around them. Data processing allows these systems to decide what to see and act accordingly.
More technically, computer vision is a field of AI that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs. The models act or make recommendations based on what they learn from the inputs.
How is computer vision different from computer vision?
There is a subtle but important distinction between computer vision and computer vision. Computer vision is based on the ML method and uses enormous processing power to apply algorithms to large amounts of data. Computer vision systems collect as much visual data as possible and then process this information to apply it to various tasks. This is what gives machine vision applications their flexibility.
Machine vision is a lighter subset of computer vision. Machine vision typically focuses on a narrow task. In manufacturing, computer vision (or robotic vision) is often used for quality control and to guide objects along an assembly line. We will discuss this later in the section on machine vision and manufacturing.
The aim of visual recognition
Computer vision aims to replicate the complexity of human vision. How? By giving computers a way to interpret and understand the world through images. Computer vision applications are based on visual artificial intelligence. Machines are trained on huge data sets of visual information in a process called ML. The only difference between computer vision and other data used in AI is that computer vision processes visual rather than contextual data.
With enough training, AI software can make sense of visual inputs, but most computer vision technology does not come close to human vision. AI still has problems with adaptability, ambiguity management and context understanding. For example, an early version of Stability’s AI model recognized that the same element was present in many photos in its training data. Its artistic generator, Stable Diffusion, began to insert that element into photorealistic images. Unfortunately, the AI did not know that the element in the image was the Getty Images logo and violated the Getty trademark. Stable Diffusion also admitted to training with Getty photos without permission.
That said, computer vision technology is impressive and has many use cases. Artificial intelligence is better than humans at some visual tasks and is almost always faster. But before we analyze the use of computer vision in different areas, let’s look at how computer vision technology works today.
How we “see” the world today through the eyes of machines
Computer vision systems use a combination of hardware and software to extract, analyze, and understand visual information. This information can come from an image or a sequence of images (in other words, from a video). In very simple terms, the steps of computer vision include:
- Training: an algorithm is trained on massive sets of visual data.
- Input: cameras, sensors and other imaging devices capture visual data.
- Processing: the computer vision algorithm analyzes the input and identifies patterns, objects and relationships.
- Decision-making: the machine uses analysis to make informed decisions or predictions.
- Action: the machine performs a task based on visual analysis.
Computer vision has been around for decades, but recent developments in artificial intelligence have improved real-time processing and decision making. Thanks to modern neural network technology, computer vision systems have gone from 50% accuracy to 99% accuracy in less than 10 years. In some cases, the changes are so successful that computer vision is comparable to human vision in recognizing and responding to visual input.
Let us consider these processes in computer vision and the complex tasks they perform.
Recognize and classify objects
Computer vision techniques can identify and classify objects within images with impressive accuracy. These include faces, animals, vehicles, specific products and even complex scenes.
Some examples taken from everyday life:
- Snapchat: filters can make you look like a cat in a hat because the app recognizes your face
- iPhone Photos: this app customizes photo collections by classifying photos into categories.
Motion tracking and detection
Motion tracking and motion detection are fundamental capabilities of computer vision systems. Motion tracking and motion detection help machines interpret what exists in an image and understand when and how the scene changes. This dynamic understanding of an image over time opens up a wide range of applications for computer vision, including:
- Home security cameras: motion-activated sensors can turn on the camera to record suspicious activity.
- Autonomous vehicles (AVs): continuous scanning of the environment enables AVs to detect objects in their path, such as pedestrians, other vehicles, and potential hazards, all while navigating on busy roads.
Segmenting and analyzing images
Computer vision can be used to break down images into their constituent parts. This process, called segmentation, can mean separating the foreground from the background. It can also involve identifying specific regions of interest. This type of analysis is critical for tasks such as:
- Radiology: image segmentation helps health care providers identify abnormalities in medical images, including X-rays, MRIs, CT scans, and PET scans.
- Automatic content moderation: social media companies use computer vision to automatically detect unwanted content in images or videos.
Understanding of 3D structure and depth
Computer vision systems can also perceive depth, grasp spatial relationships of objects, decipher shapes and sizes in the real world, and build 3D models from visual data. The use of computer vision for 3D object detection opens the door to applications such as:
- Robotics: understanding the world in 3D helps robot vacuum cleaners navigate complex environments.
- Augmented reality (AR): computer vision applications with accurate depth perception and understanding of 3D can create a pass-through vision, in which virtual objects are seamlessly superimposed on the real world.
Using computer vision: seeing is believing
Although the exact figures are conflicting, research companies agree that computer vision technology is a growing market. We have seen forecasts ranging from a compound annual growth rate (CAGR) of 11 percent over the next 10 years to nearly 19 percent.
Although analysts disagree on the exact numbers, the outlook for computer vision is optimistic. The market will grow to $59.8 billion by 2033, as the chart above shows. Allied Market Research predicts that the computer vision market will reach $82.1 billion by 2032. With the proliferation of cameras in smartphones, security systems and other devices, we are generating more visual data than ever before. This vast pool of data serves as fuel for training and improvement of computer vision projects.
Benefits of computer vision
Advances in deep learning have improved the accuracy and performance of computer vision technologies. Key components of computer vision, such as open-source tools and cloud computing platform services, have made the technology more accessible and affordable. As a result, developers and companies of all sizes are building computer vision tools.
Computer vision can be used in systems that can solve real-world problems around us:
- Support real-time applications by processing visual data much faster than humans can.
- Reduce bias, fatigue, and human error by performing mundane tasks with consistent results
- Automating and scaling processes that would not be safe or feasible for humans
- Monitoring environments and equipment to ensure safety and prevent accidents.
- Extracting business information from visual data analysis to support decision making and strategic planning.
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