computer visions

what are computer visions and what challenges does it have to face

AI is not a secret topic anywhere, people everywhere are widely talking about it and some are even trying to convert this topic into an educational one. Even though AI has become a popular topic, it is still not completely clear how the interaction processes work or the technologies used in such a twisted and complicated field. The only thing that is somehow a little easy, for us, to understand about AI is its visions. 

What are these visions? The only part or subset of AI we ordinary people can recognize is called computer visions. Well, it was easy to write but how will you recognize it? The recognition process is simple too; anything that involves photos or videos, or is visual in any way, then you can proudly say that it is the subset of AI, computer vision.

Computer visions

  • If you find an intelligent experience filled with visuals, then computer visions are working behind them.
  • The subset of artificial intelligence focuses on developing techniques that will allow all kinds of machines to capture digital images and refine video content.
  • It is not shocking to learn that people now prefer contents that contain images or videos.
  • A report published in 2020 said that the pandemic has made us glued to our mobile phones even more and the content people preferred more was the one with visuals. Even the students who are now dependent on online classes are getting more attracted to online assignment help that explains the concepts with graphics. According to Hub spot, people want reality in visual formats and according to Hub spot, more than 54% of users want their favorite websites to create more content with videos and images.
  • It is safe to say that, computer vision is or will never be going to run out of business, AI won’t allow it.

Computer visions and natural visions

Computer visions and natural visions

AI is like an ocean, the topics and fields AI has are never-ending and not at all understandable unless you are a tech guru or are working at AI. That’s why more people are interested in computer vision because this is the only thing about AI that we can understand a little without burying ourselves in AI. So, it is nothing but interesting to know how computer vision works in simple words.

We have our ‘natural tool’ to obtain visual information which is more than enough, but compared to our natural tool, the ability of machines lags far behind.

Still, the machines are never not useful.

  • Computer visions, a subfield of AI, create visions by mimicking our natural processes.
  • Computer vision means machine learning at its best.
  • In simple words, if artificial intelligence is allowing its machines to ‘think’ and ‘work’, then it is the computer vision that allows machines to ‘see’ and ‘create’.
  • The whole process in three words can be written as: computer vision fetches visual information, handles it according to the AI, and then interprets it. 
  • Technically, it authorizes machines to comprehend an image, then make sense of it in codes, and finally respond to this visual information collected. 
  • All the algorithms needed for this process to happen, when simplified, match the natural neural networks.

Working of computer visions

Working of computer visions

Now, it is almost impossible to think about machines that only work under AI and not computer visions. It has become a major part of AI and is widely used in industries like- agriculture, manufacturing, medicine, e-commerce, etc.

The main task of computer vision is image classification. Computers can easily generate and learn features, characteristics, and properties of images due to the use of deep learning in image recognition. Based on this, machines try their level best to recognize the features of an image and show us an image with maximum similarity.

The first step

First, machines divide an image and handle them individually. Then it tries to recognize the pattern that is repeated in between the visual stimuli of images. There is also object tracking and classification for videos and object detection for images.

The second step

As mentioned earlier, it is machine learning which means going through multiple data before getting what you wanted. That’s why deep learning is important for computer vision so that they don’t face any problems while separating two images.

machine learning flow

AI is provided with tons of images, to separate them category-wise, is the real task. And that’s the job of computer visions. Computers put these images through various processes to distinguish between the images. They do that by looking at the lines or edges between the lines, the dark or light part of the image, and shapes or even faces of the image. Computers interpret images as pixels and through pixels moves the image to a neural network where the image will get ready for the display process.

Popular fields where computer visions are dominating 

  1.  Enhances the OCR-ed images.
  2. AR-enhanced videos and Images.
  3. Robots in the retail field.
  4.  Best for medical imaging tools.
  5. Boosts the agriculture industry.

According to a report, more than 3 billion images are shared online daily, giving us a clear idea of how computers visions are crawling Their way up to the top.

The challenges

Without computer vision, AI alone can’t make a machine work. We know that computers can only see digital image representation, and that’s a little challenging. Because humans can understand the semantic meaning of an image, whereas machines rarely do, they only understand pixels.

The human brain can easily differentiate between the components of an image and can analyze them in sequence. Our natural neuron network system is responsible for image creation. That’s why building a technology that will imitate this neuron network and covers the semantic gaps, took decades. Even if it took years to build, once it was created, it proved to be the best creation in machine learning.


Computer visions are a part of AI that we can try to understand, that too only if we just slide through the processes, deep knowledge is not that simple. Still, this technology is one of the most straightforward, to-the-point tech concepts ever created.

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