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The Future of Vision: Machines vs. Humans

The future of vision is a hot topic in the scientific world. Will machines eventually surpass humans at vision? Or will computers never be able to do what our brains can do naturally? In this blog post, we explore the history and science behind computer vision and how it came to be so powerful today.

Photo by Dasha Yukhymyuk on Unsplash

What is computer vision?

Computer vision is the ability of computers to interpret and understand digital images. This can be done in a number of ways, including identifying objects in an image, recognizing facial features, or detecting movement. Computer vision has become increasingly important in recent years thanks to the growth of artificial intelligence (AI). With AI, computer vision can be used for tasks such as facial recognition, automatic tagging of photos, and even driverless cars.

How did computer vision come to be so powerful?

Computer vision has its roots in the early days of computing. In the 1950s, scientists began to develop ways for computers to interpret images using mathematical algorithms. This was a difficult task at the time, as computers were not very powerful and images were stored in a very different way than they are today. It wasn’t until the late 1990s and early 2000s that computer vision really began to take off. This was due, in part, to the development of more powerful computers and the growth of the internet. With more people using the internet, there was a greater need for ways to process and understand all the images that were being uploaded.

What challenges does computer vision still face?

Despite its rapid growth, computer vision is still facing some challenges. One of the biggest challenges is understanding natural images. This involves understanding things like lightness and color, as well as the context in which an image is taken. This can be difficult for computers because there are so many different variables at play, and the images often contain noise that needs to be accounted for. A second challenge involves understanding real-world objects such as road signs or animals in their natural environment — this type of information cannot always be found on Google; it must instead be learned from data sets that are specifically created for this purpose.

“…our eyesight is still superior to technology when it comes to vision. And we’re not going anywhere soon!”

How computers see things and how they compare to humans

So far, we have seen that computer vision is the ability of computers to understand and interpret digital images. This can be done in a number of ways, including identifying objects in an image, recognizing facial features, or detecting movement. How exactly do computers go about doing this?

Well, as we mentioned earlier, computer vision relies on mathematical algorithms to process images. These algorithms work by breaking down an image into a series of small square blocks, or pixels. For each pixel, the algorithm assigns a number that represents how bright or dark it is. It then compares these numbers to other numbers in the image and looks for patterns. By doing this, the computer can create a model of the image that it uses to identify objects or changes in the picture.

Humans, on the other hand, see things differently than computers do. Humans are especially good at seeing very small details and understanding context when looking at an image. They can also easily understand images taken from different angles or under different lighting conditions. Finally, humans can look at a picture and easily identify the objects in it, even if they are small or partially covered.

Since computer vision is based on mathematical algorithms rather than human-like understanding, computers have trouble with these types of tasks. This illustrates how far away we still are from creating artificial intelligence that can see things like humans do. However, scientists are working hard to close this gap, and we can expect to see significant advances in computer vision in the years to come.

Where do we go from here with this technology?

So where does computer vision go from here? Well, one of the most exciting applications of this technology is in the field of driverless cars. Driverless cars rely on a number of different sensors to detect their surroundings, including cameras and lasers. The computers inside the car use these sensors to create a model of the surrounding area and then make decisions based on that model. This technology is still in its early stages, but it has the potential to revolutionize transportation.

Computer vision is also being used in a number of other applications, including security, healthcare, and manufacturing. In the future, we can expect to see even more uses for this technology as scientists continue to develop new ways to use it. So far, computer vision has shown itself to be a powerful tool that can help us better understand the world around us. With continued research and development, it is sure to become even more ubiquitous in our lives.

In the future, it’s possible that machines will be better at seeing than humans. They’ll have memory and won’t need to process information in real-time. But for now, our eyesight is still superior to technology when it comes to vision. And we’re not going anywhere soon! provide you with an easy way to build image datasets.
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Yaniv Noema

Yaniv Noema

I’m a computer vision 💻👁️engineer who likes to write about artificial intelligence, machine learning, image processing, and Python🐍

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