• Understanding Computer Vision: An Introductory Guide

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A machine that can mimic the way a human sees has always fascinated people. Reconstruction of the workings of the human eye began in the 1950s, and much progress has been made since then. Computer vision is now a feature that can be found in mobile phones via cameras or various e-commerce applications.

Most of the advances in computer vision are due to the improvement of revolutionary technologies such as artificial intelligence (AI) and its many subsets, including machine learning (ML) and deep learning (DL). With the use of computer vision becoming ubiquitous, the market is expected to reach a value of $48 billion by 2022. It is currently considered one of the most promising technologies in the field of UX or user interface.

Definition of computer vision

Computer vision is a part of artificial intelligence that teaches computers how to process, analyze and interpret images, videos and other visual data. Machines with deep learning models that use digital images from video and still cameras can classify, recognize and respond to objects.

AI subfields such as machine learning and deep learning are also supported by continuous learning (CL); as the name suggests, AI models help with continuous learning from the data stream.

Computer vision systems are used for such tasks:

  • Object tracking. The technology recognizes objects of a certain class, such as people, vehicles or animals. For example, the recognition of a particular vehicle among other vehicles.
  • Identification of the object. Examines visual content and detects specific objects in a video or photo, such as. For example, the face or fingerprint of a certain person.
  • Image Classification. Examines visual content and categorizes a particular image in a video or photo, for example. For example, assign a label to all other objects in the video or photo.

This recommended reading also lists other applications of computer vision.

How computer vision works

One hypothesis about how your brain recognizes an object is that it relies on patterns to interpret each object. This idea is used in computer vision systems to simulate the human brain. Currently, computer vision algorithms are rooted in pattern recognition. Computers learn by receiving large amounts of visual information; they then find patterns in objects after classifying them.

For example, if you give the computer images of an object, such as a coconut palm, the computer will analyze the images, recognize the patterns of all the coconut palms it has previously analyzed, and finally create a model of the coconut palm. Next time, the computer recognizes the coconut palm based on the images it encounters.

Development of computer vision

In experiments in the 1950s, early versions of neural networks were used to perceive the edges of objects. This early computer vision was also used to categorize simple objects such as squares and circles. In the 1970s, computer vision was mainly used for interpreting handwritten and typed texts. This was an early version of optical character recognition used to help blind people interpret written text.

The methods of that time were simple compared to the analyses performed by modern computer vision. But it was very labor-intensive for human operators, who had to manually provide data samples for analysis. Not only was this process time consuming, but computing power was not adequate at the time, so the error tolerance was very high. AI and machine learning were also in their infancy.

With today’s computing power and lightweight algorithms, solving overly complex problems is a breeze. Furthermore, due to the amount of visual data available in the public domain, computer vision systems are constantly learning, which allows for further improvements in computer vision. Due to the enormous amount of data that computers process, computer vision systems have become increasingly sophisticated. Among other things, it can now recognize specific people in digital images.

Computer vision has been integrated into various areas of human life. Examples of the use of this technology include facial recognition, self-driving cars, and content organization. It is also used in almost all industrial sectors, e.g. healthcare, agriculture, retail, banking and finance, and many others.

The advent of deep learning

The modern version of computer vision is based on Deep Learning, which uses algorithms to extract knowledge from large amounts of information. Machine learning is based on AI, which is the foundation of both technologies. Deep learning combines well with machine learning, a subset of AI.

Thanks to Deep Learning, computer vision works efficiently. It uses an algorithm called neural network which is effective in retrieving patterns from available datasets. The neural network algorithm is inspired by the interconnection of neurons in the human cerebral cortex. Machine learning algorithms are used to process the data, with Deep Learning being based on artificial neural networks (ANNs).

Conclusion

Computer vision is one of the results of advances in the field of artificial intelligence. The development of sophisticated algorithms, increased computing power and the availability of petabytes of data have contributed to a significant improvement in computer vision in recent years.

It is used in many industries that have helped people a lot such as healthcare, retail, financial services, agriculture and many others. The potential is almost limitless. Computer vision is one of those crucial advances in the development of artificial intelligence that you only see in novels.

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