How Can We Use Machine Learning and AI Models to Improve Computer Vision?

In fact, artificial intelligence is a set of technologies based on mathematics, hardware, and software that allows you to automate the solution of routine tasks.

Not everyone can handle these new technologies. And then ai software developer at Unicsoft comes to the rescue. There you can learn everything about artificial intelligence.

The association of the mathematical approach with neural networks dates back to the 1940s when Pitts and McCulloch proposed the simplest mathematical model of a neuron. Simultaneously, a simple learning algorithm appeared.

The next surge of interest occurred only in the 90s when computing power and new good mathematical algorithms appeared that made it possible to solve problems of recognition and prediction. And in 2014, recognition technologies received literally the third birth because we have learned to solve such problems in order of magnitude better than before. But the association with neurons has survived to this day.

Technology has gone a long way. But there are still many problems with recognition systems. Algorithms need to be improved to make them work more efficiently. There is a lot of room for not only an engineer but also a scientist.  But let’s start with how it works.

Computer Vision

Computer vision is an applied field, an integral part of artificial intelligence. In theory, we expect from computer vision the ability to imitate a person’s ability to recognize objects in a photo — the ability to understand where is the text, where is the face, and where is the building.

Given the combination of recognizable elements in the photo, a person has a lot to say. He sees that the sky is blue, the flags do not flutter in the wind, which means that there is no wind and the weather is sunny. I wish computer vision systems could do this.

The Turing test for computer vision systems is to answer any question about an image that a person can answer.

The first algorithms for computer vision appeared long ago. A typical example is one of the simplest Viola-Jones face detectors, which marks the position of people in the frame.

The operation of the face detection algorithm in cameras

This algorithm is, in a sense, non-learning. Well, at the moment we are seeing a boom in algorithms that are based on more complex principles.

How Computer Vision Systems Work

A digital image is a matrix, where each pixel is some element containing a number. In the case of a black and white image, this is a number between 0 and 255, which reflects the intensity of the gray.

For a color image, this is usually a combination of three colors. Even in the century before last, the first color photographs were simultaneously filmed on three cameras in different colors, and then the resulting frames were combined. And until now, color images are often decomposed into the same three colors — red, green, and blue.

The Categorization Problem

Computer vision allows solving problems of recognition. In fact, this is a basic categorization task where we tag a photo from a predefined set of categories.

This task is of two types: binary (for example, is there a person in this picture) and more complex (what types of plankton are in the picture). It happens that simultaneously with the classification of an object, we must note where it is.

Let’s say we have a picture. An engineer would approach recognition in the following way: he would start checking what is in this image. For example, what are the objects that have an oval shape? For this, he would choose some features that would take on large values on oval-shaped objects.

This is an artificial example, but it is important to understand the principle here. When we count these features, they will go to the input of the classifier. If among them there are those that take on large values, we say that there are certain objects in the image, and they are in such and such a part.

A typical example of a classifier is what is called a decision tree.

Decision trees of this type can also be built in more complex cases. For example, when issuing a loan, they will have a lot of nodes where branches take place.

In practice, they usually combine many decision trees, that is, they receive answers from each, and then they carry out something like a vote.

When recognizing a photo (looking for an answer to the question of whether there are people in the photo), we can apply exactly the same approach – we count the features and send them to the decision tree to get the final answer.

Summing up, we can say that Artificial Intelligence will definitely help us in recognizing objects in pictures and photographs, which is a huge breakthrough. And definitely artificial intelligence technologies will continue to develop, especially in computer vision systems.

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