What Is Computer Vision?

Computer vision is a field of artificial intelligence (AI) that applies machine learning to images and videos to understand media and make decisions about them. With computer vision, we can, in a sense, give vision to software and technology.

What Is Computer Vision?

Computer vision refers to the ability of machines to identify patterns within visual data and glean meaningful insights. Depending on the context, machines can use cameras, sensors, smartphones and other devices to compile data for training and analysis. They can then perform tasks like reading written text, recognizing specific faces in images and locating particular objects in a video feed. 

The ultimate goal of computer vision is to enable machines to see and perceive the world similarly to humans. Once machines have been equipped with computer vision and trained to hone their abilities, they can use computer vision to detect anomalies, direct self-driving cars, monitor equipment and analyze athletic performance, among other use cases.  

History of Computer Vision

Efforts to analyze visual data began in earnest in the 1960s, despite limited computational resources at the time. The field gained more traction in the 1970s with the introduction of the Hough transform method, which enabled researchers to detect lines, circles and other simple shapes in images. This progress gave way to more advanced algorithms in the 1980s. For example, computer scientist Kunihiko Fukushima created the Neocognitron — an early version of convolutional neural networks (CNNs) that could recognize patterns in images. 

Researchers in the 1990s built on this foundation, developing methods for object recognition and facial recognition. These initiatives culminated in the Viola-Jones face detection model of the early 2000s, which was far more accurate and efficient than prior models and considered one of the first real-time face detection systems. However, deep learning defined the strides made in computer vision in the 2000s and 2010s, with AlexNet and DeepDream being two examples of how neural networks propelled the field forward.  

OpenAI’s launch of GPT-3 in 2020 heralded the age of chatbots. Although GPT-3 was designed for text-based tasks, it demonstrated some vision capabilities. More recent versions of the GPT models that power ChatGPT possess multimodal capabilities, and other chatbots like Claude and Gemini are now equipped with the ability to process visual data like images and videos, in addition to other data types.

How Does Computer Vision Work?

Computer vision programs use a combination of techniques to process raw images and turn them into usable data and insights.

The basis for much computer vision work is 2D images, as shown below. While images may seem like a complex input, we can decompose them into raw numbers. Images are really just a combination of individual pixels and each pixel can be represented by a number (grayscale) or combination of numbers such as (255, 0, 0—RGB).

Once we’ve translated an image to a set of numbers, a computer vision algorithm applies processing. One way to do this is through CNNs which use layers to group together the pixels to create successively more meaningful representations of the data. A CNN may first translate pixels into lines, which are then combined to form features such as eyes and finally combined to create more complex items such as face shapes.

Why Is Computer Vision Important?

Computer vision has been around since as early as the 1950s and continues to be a popular field of research with many applications. According to Statista, the global computer vision market is expected to exceed $29 billion in 2025 and is on pace to reach nearly $47 billion by 2030. 

The importance of computer vision comes from the increasing need for computers to be able to understand the human environment. To understand the environment, it helps if computers can see what we do, which means mimicking the sense of human vision. This is especially important as we develop more complex AI systems that are more human-like in their abilities.

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Common Computer Vision Tasks

Computer vision can be used to perform a wide variety of tasks, with the following being some of the main ways it’s used. 

Optical Character Recognition

Optical character recognition (OCR) refers to extracting data from images and converting it into text that computer applications and machines can read. OCR can identify numbers and letters from images, scanned documents and image-only PDFs, arranging these numbers and letters accordingly. A well-known example of this is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language. 

Object and Image Classification

Object classification refers to the ability of a machine to identify specific objects and place them into categories. For example, a system may be trained to identify and sort humans from objects in images. This principle can be extended to image classification, which refers to organizing images based on distinct traits. An image classifying system may separate images of apples from images of bananas.  

Object Tracking

Object tracking uses models to visually follow an object through a video feed. This process employs object detection, creating a bounding box around the object of interest and assigning it an object ID. A system can then keep track of this object and record its location and movements, making object tracking useful for surveillance cameras. 

Content-Based Image Retrieval

Content-based image retrieval is the ability to sift through a database in search of particular digital images. In this case, models use identifiers like labels, keywords, metatags and metadescriptions to navigate the database. A system can then pull the appropriate images when following a command like, “Retrieve all images of trucks.” 

Object Detection

In object recognition, an algorithm takes an input image and searches for a set of objects within the image, drawing boundaries around the object and labelling it. This application is critical in self-driving cars, which need to quickly identify their surroundings to decide on the best course of action. 

How Is Computer Vision Used?

Computer vision is often used in everyday life, and its applications range from simple to very complex.

Facial Recognition

Facial recognition uses computer vision to recognize features of human faces, picking out humans in images and videos. This has made face recognition an impactful tool for areas like hospitality, manufacturing and retail. Mobile phone developers have taken this technology one step further, building a face ID feature where users can unlock their phones using their unique facial features. 

Self-Driving Cars

The ability of computer vision to single out individual people and objects makes it a major safety feature for self-driving cars. Vehicles can identify passengers, traffic signs and other vehicles to become aware of their surroundings. They can then take appropriate actions as needed to follow traffic laws and navigate constantly changing environments.

Robotic Automation

When paired with machine vision, computer vision enables robots to view and process their surroundings. Robots can then perform tasks like sorting packages, arranging parts on an assembly line and keeping track of inventory. This allows companies to implement robotic process automation across their operations. 

Medical Anomaly Detection

The capabilities of medical imaging can be enhanced with computer vision. For example, researchers have explored using computer vision to analyze chest X-rays. They found it to be especially helpful in detecting issues like tuberculosis and respiratory infections, increasing the accuracy of medical imaging and leading to better outcomes for patients.

Sports Performance Analysis

Computer vision has the potential to fulfill a number of roles in the sports industry. Organizations can use computer vision to track the movements of athletes, so they can identify signs of injuries and take proactive measures. It can also take on the role of referee, automating calls to take human error out of the picture. 

Manufacturing Fault Detection

In manufacturing settings, computer vision systems can be used to monitor machines and equipment, looking for signs of wear, tear and other damage. They can then alert staff when certain machines need repairs, supporting predictive maintenance and helping companies avoid costly disruptions. 

Agricultural Monitoring 

Precision agriculture has come to rely on computer vision for needs like monitoring the conditions of crops, tracking soil quality and detecting pests. Agricultural robots depend on computer vision as well, using the technology to identify crops that are ready for picking and safely harvest them. 

Plant Species Classification

Computer vision can aid in accurately identifying plant species, using deep learning models and CNNs. Not only does this support ecological studies, but it also contributes to conservational efforts, agricultural initiatives and pharmaceutical use cases. 

Text Parsing

OCR has become a key use of computer vision, enabling industries to automate various processes. With this technique, grocery store self-checkout machines can scan food labels, banks can extract information from documents to quickly process loan applications and warehouses can automate the process of scanning inventory labels.  

Augmented Reality Contacts and Glasses 

Computer vision is a core component of augmented reality (AR), which uses the technique to detect physical objects and map out various environments. The technology has led to newer inventions like AR contacts and smart glasses, which use computer vision to identify objects, process written text and more. 

What Are the Risks of Computer Vision?

As with all technology, computer vision comes with risks that need to be considered alongside the possibilities it offers. 

Data Privacy Concerns

Facial recognition technology uses computer vision to identify specific people in photos and videos, and this ability has fueled concerns regarding data privacy. It’s not always clear to the general public when or how facial recognition technology is employed, bringing up issues around consent and transparency. 

Fears Around Bias and Discrimination

AI systems have been known to reproduce societal biases, so applying computer vision systems without taking precautions can lead to discrimination. For example, Rite Aid deployed facial recognition that disproportionately labeled women and people of color as potential shoplifters, resulting in a five-year ban against the technology in company stores. 

Security Risks When Used by Malicious Actors

Computer vision systems unintentionally serve as another opening for bad actors to exploit. If hackers find a way to infiltrate AI systems equipped with computer vision, they can use these systems to conduct surveillance, compile photos and videos of people without their consent and perform other dangerous activities.  

Potential for Mistakes

Any kind of AI-based technology is prone to hallucinations and errors, which can have lasting consequences in certain situations. For instance, a computer vision system may falsely detect no signs of disease during a medical screening when there actually is a disease. These kinds of mistakes can happen on a larger scale if users don’t exercise caution. 

Lack of Personnel Experienced in AI 

Adopting computer vision solutions requires personnel with a particular skill set, and many companies don’t have these employees readily available. In fact, organizations have been desperate to hire machine learning engineers and other AI personnel needed to set up and maintain AI technologies. If a business doesn’t have the capacity to hire these types of employees, computer vision may remain out of reach.

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