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April 11, 2022

Future of Computer Vision & AI


Artur Haponik

CEO & Co-Founder

Reading time:

8 minutes

Over the past decade, computer vision driven by machine learning has burst onto the technology scene. With several mediums of perception, computers get superhuman visual power and can identify patterns from images that humans can’t. For example, in the healthcare sector, the pattern recognition prowess of computer vision is unmatched by human physicians.

Research reveals that artificial intelligence can read CT scan images and diagnose neurological disorders faster than radiologists. [2] With impressive exploits by artificial intelligence, computer vision solutions are surfacing in different sectors, and their future seems to be full of promise and unthinkable outcomes.

In this post, we look at the history and where the future of computer vision lies. Read on for more insight.

What is computer vision?

Sight and vision are often used interchangeably, although they mean different things. Sight is a sensory experience in which light signals are converted into images in the brain. Vision, on the other hand, refers to how the mind construes these images. While you can witness an event with your sight, vision helps you comprehend the importance of that event and make interpretations. [1]

What is computer vision?

Computer vision is one of the subsets of artificial intelligence that allows machines to emulate the human visual system and automate tasks involving visual cognition. By using annotated images and machine learning techniques, computers can detect and decipher data items more accurately and then prompt suitable actions based on what they “see”.

Future of Computer Vision: Evolution/h2>
Research into the field of computer vision began in the 1960s.[3] The objective was to train computers to rival human vision. But commercializing computer vision technology was a major challenge because manual training was the order of the day for early computer vision technology.

This involved feeding the computer with image training data, extracting pertinent features, and annotating these features. The data engineer would then code each module as an instruction to identify the features within the visual input.

Deep learning

Deep learning has revolutionized computer vision and scaled the technology commercially for industrial applications. It simplifies the manual extraction process by using huge sets of training data and multiple training cycles to train computers on what an object looks like.

As opposed to the manual extraction of features, the algorithm automates the entire process and automatically extracts appropriate parts. Even with previously unseen images, the deep learning model can still generate an accurate prediction.

deep learning

It might be interesting for you: Training Data for Computer Vision

Deep learning developments in computer vision can be attributed to the infinite amounts of visual data present today. The open availability of image data from various sources, like social media sites and CCTVs, has created a scenario where everything is monitored, captured, and decoded.

Feeding tons of annotated images to a computer vision algorithm teaches it to understand the actual features that constitute the bigger image. This increases the level of learning by a computer vision model and, ultimately, helps deliver accurate performance and efficiency in present computer vision applications like:

  • Medical Image processing
  • Manufacturing quality control
  • Health monitoring [4]
  • Military operations
  • Traffic analysis [5]
  • Autonomous vehicles
  • Security surveillance
  • Digitization of physical documents

The future of computer vision

The applications of present-day computer vision seemed unachievable a few decades ago. And from where we stand, there seems to be no end in sight to the capabilities and future of computer vision technology. Here’s what we can expect to see in the future:

the future of computer vision

Read more about: The latest advances in computer vision

A wider ranger of functions

Continued research and refinement of computer vision technology will see it carry out a wider spectrum of functions. The technology will be easier to train and thus have the ability to detect more images than it does now. Computer vision will also be integrated with other technologies or subfields of AI to create more agile applications. For example, the combination of image captioning applications and natural language generation (NLG) can be used to understand the objects in the environment for visually handicapped people.

Learning with limited training data

The future of computer vision technologies lies in developing algorithms that require limited annotated training data compared to current models. To address this challenge, the industry has begun exploring a few potentially pioneering research themes:

  • Developmental learning: Machine systems that manipulate their surroundings to learn via a sequence of successes and failures when carrying out important roles like navigating and grasping.
  • Lifelong learning: AI systems that capitalize on formerly learned visual concepts to attain new ones without explicit supervision.
  • Reinforcement learning: Drawing inspiration from behavioral psychology, it concentrates on how robots can master how to take suitable actions.

Common sense reasoning

Common sense reasoning entails obtaining visual common sense knowledge and applying it to answer questions on videos and images. Currently, computer vision is at the stage where it can detect and explain multiple objects in imagery.

future computer vision solutions: common sense reasoning

Seeing what is captured in an image is only the first step toward understanding digital image data in a useful way. [6] The next frontier for computer vision technologies is acquiring and utilizing visual common sense reasoning so that machines can move beyond just identifying the types of objects in image data.

In future years, the computer vision industry is expected to create explanatory computational models that can provide answers to the following questions on images and videos:

  • What is there?
  • Who is there?
  • What is the individual doing?
  • What climatic conditions are affecting their activity?

Computer vision systems should also be able to give answers to more complex questions like:

  • Who is doing what to whom and for what reason?
  • What is most likely to occur next?

Combining computer vision with robotics

Computer vision technologies will soon join forces with robots in the physical world. Over the next decade, a key opportunity lies in developing robot systems that can smartly interact with human beings to help accomplish specific objectives.

computer vision and robotics

Of course, this is closely linked to visual common sense knowledge. Remember, common sense reasoning informs how certain activities illustrate certain goals and limits. So, a robot will be able to understand a person’s objectives by weighing up the actions it sees the individual taking through common sense reasoning. For instance, a computer vision model might see an individual running in a metro station. But common sense knowledge will help the robot deduce whether the individual intends to catch the train or flee from danger.

The acquisition and representation of visual common sense will inspire the creation of robots that have social understanding. This will enable robot systems to understand how human responsibilities and objectives trigger their actions. Such robots with visual cognition skills will be used to enhance the situational awareness of different surroundings.

Learning without explicit supervision

Technology is bound to improve computer vision learning via a robot that actively explores its surroundings. In the future, robots might be informed about the class identities of the images they observe. This means that they will be able to autonomously move while trailing the objects to gather plenty of views on them without explicit manual labeling.

You might wonder how a robot will pull this off. Well, computer vision systems can presently figure out the class identity of an object through passive exposure to massive data training image sets from one object class. So, learning the alleged “affordances” of objects will occur via active interactions between the robot and the physical world.

Affordances work out the potential applications of an object. For example, if the object can be opened, such as a door, refrigerator, or soda can, or those that cannot, such as a tree or baseball. Learning the affordances of objects will allow robots to attain objectives across different environments.

Final thoughts on the future of computer vision

From healthcare and manufacturing to security, computer vision technology has permeated almost every sector of everyday life. But we’ve only scratched the surface of exploring the full potential of computer vision.

The future will see more discoveries made about the capabilities of this technology. This will pave the way for intelligent systems that rival human visual capabilities and thinking.


[1] Why good vision is so important. URL: Accessed March 27, 2022
[2] Automated deep-neural-network surveillance of cranial images for acute neurologic events. URL: Accessed March 27, 2022
[3] An overview of Computer Vision. URL: Accessed March 28, 2022
[4] M. Kumar, A. Veeraraghavan, and A. Sabharwal. DistancePPG: Robust non-contact vital signs monitoring using a camera. Biomedical optics express, 6(5):1565–1588, 2015., Accessed March 27, 2022
[5] S. Zhang, G. Wu, J. P. Costeira, and J. M. F. Moura. Understanding Traffic Density from Large-Scale Web Camera Data. arXiv:1703.05868 [cs], Mar. 2017. arXiv: 1703.05868. Accessed March 28, 2022
[6] Future Directions of Visual Common Sense & Recognition. URL: Accessed March 28, 2022


Computer Vision