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January 13, 2022

Computer Vision: What It Is and Why It Matters

Computer Vision: What It Is and Why It Matters

Here's what you need to know about computer vision and some of its common applications.

Mehreen Saeed
Mehreen Saeed
Computer Vision: What It Is and Why It Matters

Computer vision technology simulates the visual perception of living organisms, including humans. Used in domains including AI, machine learning, computer science, and mathematics, computer vision allows machines to collect, interpret, and understand visual data. 

A typical computer vision system takes as input digital visual data captured by sensors such as cameras, LiDARs, and radars, and then processes and transfers the data to a machine learning or deep learning model for further interpretation.

Why is Computer Vision Important?

Computer vision technology is shaping the future of applications such as digital library organization, security systems, autonomous robots, self-driving cars, and more.

What are the Typical Real-World Applications?

Initially computer vision applications were restricted to optical character recognition, but now the technology has made its way into other areas, including defense, manufacturing, automotive, robotics (for self-driving cars), biology, and retail. Some of the most popular applications of computer vision include:


  • Plant, weed, and insect identification
  • Agri-robotics for automatically potting and harvesting plants
  • Livestock or poultry monitoring
  • Real-time monitoring of crops and yield estimation


  • Designing store layout and product aisles by monitoring customer activity and people density estimation
  • Creating security systems for theft detection
  • Managing resources through wait time analysis and people counting
  • Building self checkout and `Just walk out` shopping experiences
  • Designing virtual mirrors for apparel that don’t require try-on rooms


  • Quality inspection and product defect identification
  • Automated product assembly
  • Object detection and counting for automated packaging


  • Medical imaging and machine-assisted diagnosis, e.g., tumor identification from medical images 
  • Computer assisted navigation and detection in image-guided or robotic surgery
  • Patient monitoring systems
  • Specialized systems for pose detection, irregular walking, or fall detection


  • Biometric authentication and verification
  • Specialized systems including face, pose, and gait recognition
  • Transportation

Autonomous vehicles

  • Autonomous drones
  • Vehicle detection and classification
  • Vehicle counting for monitoring traffic and congested areas
  • Occupancy detection in parking systems
  • License plate detection

Augmented Reality/Virtual Reality 

  • Applications in many domains including retail/online shopping, computer games, video calls, tourism and more
  • 3D modelling 
  • Scene reconstruction from still images
  • 3D layouts and industrial simulations
  • Architectural plans and modeling

Social media

  • Technology such as search, and categorizing and photo archiving

What are the Data Challenges?

Data collection, data management, and data labeling are all challenges when implementing computer vision systems.

Data collection

Machine learning algorithms, especially those used for deep learning, require large amounts of data. In some use cases, such as medical imaging applications, acquiring specialized data in large quantities can be costly and expensive. Moreover, it's not just about volume. Machine learning teams must use a variety of scenarios to account for edge cases, such as collecting data during daytime, night time, and in adverse weather conditions, especially in the case of autonomous vehicles.

Data Management

Once you've collected a large volume of data, mining all the raw data to find specific scenarios that actually improve model performance can be a challenge. Most teams either manually parse through their data or sample randomly.

Data Labeling

Once you have collected data and selected what data to label, you need to get enough of it labeled at a high enough quality for your application. Images collected in the real world can be blurry, some objects may be occluded, and poor lighting can also make images more challenging to interpret.

Where is the Computer Vision Industry Heading?

Neural networks, the machine learning models inspired by the working of the human brain and their extension in the form of deep learning algorithms, have been game changers in the field of computer vision. With these technologies, applications considered challenging or complex, such as object recognition, medical diagnosis from images, and autonomous robots, can be developed successfully.

Deep learning is computationally expensive, both in terms of CPU resources and memory, and that has led to the rising popularity of cloud-based services. Edge devices in computer vision technology are also emerging; edge computing refers to processing and computing at the source location of data generation, e.g., processing real-time sensor data for autonomous vehicles or self-driving cars. 

Computer vision is heading towards the following goals in the short term: 

  • Computer vision as a service: Many companies already offer computer vision as a service in areas including object matching, data collection, and data labeling.
  • Smart camera-based solutions: Computer vision technology can be built into smart cameras, allowing them to process and interpret data locally.
  • Natural language processing: Using phrases from a spoken language such as English to describe objects and letting the computer vision system retrieve the images relevant to those words.

Research Areas

Longer-term research efforts for enhancing computer vision technology include:

  • Adversarial examples for learning. An adversarial example is a noisy image that can lead a computer vision system to make an incorrect prediction. Research is now geared towards adversarial learning and how it can be used to improve the accuracy of computer vision systems.
  • Self-supervised learning. For many computer vision applications, labeled annotated data may be difficult or expensive to acquire. A new focus of research is to use unlabeled data for training models, where supervision is provided by the data itself.
  • Learning with a few examples. To address situations where only limited data is available for training, researchers are working toward implementing computer vision systems by data augmentation, transfer learning, or semi-supervised learning.

Reap the Benefits from your Image Data

Practitioners have barely scratched the surface of the possible future applications, and the world is just starting to see the benefits of computer vision technology in day-to-day life.

Any organization working with image data can reap the benefits of computer vision and find automated solutions that are cost effective and that save time. 

Learn more

The Conference on Computer Vision and Pattern Recognition is one of the leading conferences on computer vision, where the latest research is presented. Here are a few notable papers from the 2021 conference:

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