From one decade to another as people invented new things, they wanted to evolve a machine capable of baring human-like intelligence and also act similar like humans. With modern technological advancements this also happened to possible of giving the computer the ability to ‘see’. Yes!! They can be programmed to see and interpret. We will be coming shortly to the point that how they do it.

This basically made happen with the power of Artificial Intelligence (AI) and computational power. Computer Vision integration has taken a huge leap because of it in our day-to-day life. This computer vision integration expected to expand more to reach about 50 billion in 2022. This marvellous UX designing technology is extremely good and reliable.

We will be discussing here about what is this topic and how computer vision evolved and it can be applied to our daily lives.

What is Computer Vision?

Computer Vision is a field of computer science that enables computers to analyse image, video or in simple words, the visual data, as humans do interpret after seeing something.

It can therefore process the perceived data to perform specified actions. In this technique, Computers basically made to understand the visual data at pixel level. With use of special visual Softwares the machine-received visual data is interpreted via algorithms. Thus, the retrieved information is further processed.

Following are some examples that computer vision can easily do:

• Classifying an object. The computer scans the image or video and can classify the identified object.
• Identification. Identify the object in the visual media.
• Tracking an object. The system first matches the criteria to check if the object is present in the media or not. Then its movement in the frame area is tracked.
  

How does computer vision work?

Computer vision is implemented mimicking the function of a human brain. And how does our brain deal with this problem of visual recognition? There are many hypothesises. One suggests that our brain recognize each separate patterns to identify different objects. Computer vision systems are also centred on this.

Algorithms with pattern recognition capabilities are heavily used in Computer Vision systems. Computers are trained on large amount of visual data. Thus, processing more images and labelling them with objects and many more steps amid these finally lead to find patterns in the next possible input. e.g., if we train a computer with millions of images of apple.

At the end we would have created a model that will recognize “Apple”. Now Computer will be able to identify each time if we are giving it a picture of an apple or it isn’t an apple. And the results will be more accurate based on the training images.

Computer Vision Evolution:

We want to emphasize on the fact that computer vision isn’t a new technology. The first experiment on this concept was started back in 1950s. That time it was used to operate to understand written and printed words. At that time, the concept of using computer vision and implementation is rather simple but used to take a lot effort and time.

As human operators have to provide the sample training data manually for analysis. And manually entering such a large amount of data is obviously pretty hard and time consuming. Addition to this, back then the available computational power is not that high so the error margin for this process / analysis was very high.

Today the latest tech doesn’t limit us in the case of computational power. Cloud Computing with joining hands with strong and efficient algorithms power us to solve even more complex problems. Though this is not the only advantage we have nowadays.

Recently the huge databases generated by daily posting, blogging, messaging and many social media platforms has built up a huge database of texts and resources from which you can pull the data. This huge database also contains a lot images related to anything that you may want to use to train your Computer Vision Algorithm.

A point to note about this thing is, this database has been growing every passing hour. So, there will be nothing to worry about less training data.

Revolutionary Deep Learning:

Understanding computer vision includes understanding the way it works. So, the way it works basically depends on deep learning algorithms. This latest way is a revolution in technical era. Deep learning is a part of machine learning which analyses algorithms more efficiently to produce efficient solutions out of the entered data from any big database.

The question arises here that… what is machine learning? So, machine learning is also part of artificial intelligence. This artificial intelligence in its glory is basis of both machine learning and deep learning. Let’s get back to the topic where we are discussing about Deep Learning. Deep learning is a more efficient way for performing Computer Vision.

It has a method or a particular type of algorithm called Artificial Neural Network which will be useful for this case. This artificial neural network or ANN will understand the pattern in the image or video and then it will predict an answer whether it’s a cat or a dog or whatever you have trained and developed the algorithm to put as output.