What Is Computer Vision? A Beginner’s Guide in 2024

Computer vision is a fascinating branch of artificial intelligence (AI) that uses machine learning and neural networks to help computers and systems make sense of digital images, videos, and other visual inputs.

Think of it as teaching machines to see and understand the world around them, allowing them to spot defects or issues and even make recommendations or take action when needed.

Computer vision works similarly to human vision, but there’s a catch. While humans have the benefit of a lifetime of context to tell objects apart, judge distances, and notice movement or oddities in an image, machines have to learn all this much faster.

Instead of using retinas, optic nerves, and a visual cortex, they rely on cameras, data, and algorithms. A well-trained system can inspect products or monitor processes at lightning speed, often catching tiny defects or issues that humans might miss.

You’ll find computer vision in action across various industries, from energy and utilities to manufacturing and automotive. And it’s not slowing down—the market for this technology is projected to reach a whopping USD 48.6 billion by 2022.

How does computer vision work?

Computer vision needs a lot of data. It analyzes this data repeatedly until it can recognize and distinguish images. For example, to train a computer to recognize car tires, you need to show it many pictures of tires. Over time, it learns to identify a tire, even spotting defects.

Two key technologies make this possible: deep learning and convolutional neural networks (CNNs).

Machine learning uses models that allow a computer to learn from visual data. By feeding enough data into the model, the computer teaches itself to distinguish one image from another. This means it learns on its own, without needing explicit programming for each image.

A CNN helps the model by breaking down images into tiny parts called pixels. Each pixel gets a tag or label. The CNN uses these labels to perform convolutions, which is a mathematical operation that helps the model make predictions about the image.

The neural network checks the accuracy of its predictions through many iterations until it starts getting them right. This process enables the model to recognize images similar to how humans do.

Think of it like a person spotting an image from a distance. A CNN first notices hard edges and simple shapes, then adds more details with each iteration.

While CNNs are used for single images, a recurrent neural network (RNN) is used for videos. RNNs help computers understand how pictures in a series of frames are connected.

The History of Computer Vision

Here is the history of Computer Vision over the years:

Early Experiments and Discoveries (1950s-1960s)

Scientists and engineers have been working on ways for machines to see and understand visual data for about 60 years.

The journey began in 1959 when neurophysiologists showed a cat various images to observe its brain responses. They found that the cat’s brain responded first to hard edges or lines.

This discovery meant that image processing starts with simple shapes like straight edges.

Around the same time, the first computer image scanning technology was developed. This technology allowed computers to digitize and acquire images.

By 1963, computers could transform two-dimensional images into three-dimensional forms. The 1960s also saw the emergence of AI as an academic field, sparking the quest to solve the human vision problem.

Milestones in Text Recognition (1970s-1980s)

In 1974, optical character recognition (OCR) technology was introduced. OCR could recognize text printed in any font or typeface.

Similarly, intelligent character recognition (ICR) used neural networks to read handwritten text. These technologies have since been used in document processing, vehicle plate recognition, mobile payments, and more.

In 1982, neuroscientist David Marr established that vision works hierarchically and introduced algorithms for detecting edges, corners, and curves.

Around the same time, computer scientist Kunihiko Fukushima developed the Neocognitron, a network of cells that could recognize patterns and included convolutional layers in a neural network.

Advances in Object and Face Recognition (2000s)

By 2000, researchers focused on object recognition, and by 2001, real-time face recognition applications emerged.

Throughout the 2000s, the standardization of tagging and annotating visual data sets improved.

In 2010, the ImageNet data set was released, containing millions of tagged images across a thousand object classes. This data set became a foundation for CNNs and deep learning models used today.

Breakthrough with AlexNet (2012)

In 2012, a team from the University of Toronto entered a CNN called AlexNet into an image recognition contest.

AlexNet significantly reduced the error rate for image recognition, leading to error rates falling to just a few percent. This breakthrough marked a major milestone in the field of computer vision.

Computer Vision Applications

There’s a lot of research happening in the computer vision field, but its impact goes far beyond labs. Real-world applications show just how crucial computer vision is across various industries, including business, entertainment, transportation, healthcare, and daily life.

A major driver for these applications is the flood of visual data from smartphones, security systems, traffic cameras, and other devices.

Although much of this data is currently unused, it has the potential to revolutionize operations across industries by serving as a test bed for training computer vision applications.

Business and Entertainment

  • IBM’s My Moments for the 2018 Masters Golf Tournament: IBM Watson analyzed hundreds of hours of footage to identify significant shots. It then created personalized highlight reels for fans, enhancing their viewing experience.

Language Translation

  • Google Translate: This app allows users to point their smartphone camera at a sign in a foreign language and get an instant translation into their preferred language. This feature makes navigating foreign environments much easier.


  • Self-Driving Vehicles: Computer vision is essential for the development of autonomous cars. It helps the vehicle’s cameras and sensors identify other cars, traffic signs, lane markers, pedestrians, bicycles, and more. This technology is critical for the safety and functionality of self-driving cars.

Manufacturing and Quality Control

  • IBM and Verizon Partnership: IBM is using computer vision technology with partners like Verizon to bring intelligent AI to the edge. This collaboration helps automotive manufacturers detect quality defects before vehicles leave the factory, ensuring higher quality standards and reducing recalls.

These examples highlight how computer vision is becoming integral to various aspects of our lives, transforming how we interact with technology and improving efficiency and safety across multiple sectors.

Computer Vision Examples

Many organizations lack the resources to fund their own computer vision labs, create deep learning models, and develop neural networks. They may also not have the necessary computing power to process large sets of visual data.

Companies like IBM are stepping in to help by offering computer vision software development services. These services provide pre-built learning models from the cloud and reduce the demand on computing resources.

Users can connect to these services through an application programming interface (API) to develop computer vision applications.

While accessing resources for developing computer vision applications is becoming easier, it’s crucial to define what these applications will do early on. Understanding specific computer vision tasks can help focus and validate projects, making it easier to get started.

Here are a few examples of established computer vision tasks:

Image Classification

Image classification involves recognizing and categorizing objects within an image. For instance, it can identify a dog, an apple, or a person’s face. Social media companies might use this to automatically detect and segregate inappropriate images uploaded by users.

Object Detection

Object detection uses image classification to identify and count the occurrences of certain objects in an image or video. Examples include detecting defects on an assembly line or identifying machinery that requires maintenance.

Object Tracking

Object tracking involves following an object once it has been detected, using images captured in sequence or real-time video feeds. Autonomous vehicles, for example, need to classify, detect, and track objects such as pedestrians, other cars, and road infrastructure to avoid collisions and obey traffic laws.

Content-Based Image Retrieval

Content-based image retrieval uses computer vision to search and retrieve images from large data stores based on their content rather than metadata tags. This task can include automatic image annotation, replacing manual image tagging. It is useful for digital asset management systems, increasing the accuracy of search and retrieval.

Summary: What is Computer Vision?

Computer vision is a part of artificial intelligence (AI) that helps machines understand visual data. This includes images, videos, and other visual inputs. It uses machine learning and neural networks to teach computers to and analyze visual information.

This process is similar to how humans see and understand images. It allows machines to recognize objects, find defects, and make decisions based on what they see.

The development of computer vision started in the late 1950s. Scientists began by studying how the brain processes visual data.

Over the years, technologies like optical character recognition (OCR), deep learning, and convolutional neural networks (CNNs) have improved computer vision. Today, it is used in many areas. 

Computer vision is used for tasks like image classification, object detection, object tracking, and searching for images based on content. It is becoming more important in many industries. This technology is transforming how visual data is used and improving efficiency and accuracy.