What is Computer Vision? - An Explanation With Examples
What is Computer Vision? - An Explanation With Examples
Computer vision is a field of artificial intelligence (AI) and is the foundation of many existing artificial intelligence technologies that provide visual analyses. This technology allows computers to see and analyze objects, just like humans. In this article, we will examine how computer vision works, and we will also take a look at essential examples operating with this technology.
Computer Vision Definition
Computer vision can be considered a branch of artificial intelligence technologies. This highly interdisciplinary and complex field aims to enable computers to make sense of visual data. Technologies such as visual data processing, visual data analysis, and classification are based on computer vision.
Computer vision technologies often use Deep Learning algorithms. However, it can also work with Convolutional Neural Networks, which we can count within the scope of Deep Learning. This allows the technology to self-learn and operate like the human nervous system.
However, computer vision technologies also utilize Machine Learning algorithms in some cases. For example, self-driving cars learn by collecting visual data with machine learning.
History of Computer Vision
Computer vision emerged in the 1960s in universities working on artificial intelligence and is considered to be the forerunner in this field. The aim of the technology was to enable computers to see and "describe" what they see, just like humans. Although the human visual system was imitated at first, the first examples of computer vision technology were quite simple compared to today. First trials, based on connecting a camera to the computer and describing what it sees, developed in a short time.
Especially in the 1970s, features such as the analysis and labeling of objects that we use today were developed. By the 1990s, algorithms and developing mathematical grounds had become the basis of technology. Also, during this period, 3D research became widespread, and studies on the three-dimensional perception of objects using cameras were advanced.
How Does Computer Vision Work?
So far, we have learned a little about what computer vision is and how it came about. Now we can take a detailed look at how this technology works. From a different perspective, computer vision is a field of engineering that aims to develop machines that can do what the human visual system is capable of doing.
Computer vision, with the support of artificial intelligence, trains computers to understand, interpret, analyze and classify visual data. Computer vision, with the support of artificial intelligence, trains computers to understand, interpret, analyze and classify visual data. It is a very important process for computers to make sense of visual data. With computer vision, machines can make sense of and categorize various visual material, from photographs to videos or live streams. But how do machines do this?
As we said before, computer vision is based on the imitation of the human visual system. In here, the question of"How does the human visual system work?" arises. The most popular answer is that our brain perceives objects according to "patterns ."Similarly, computer vision algorithms used in machines are based on pattern recognition.
It would be appropriate to clarify a little about the pattern. Algorithms need to be fed with data. Suppose you feed a computer vision algorithm with many photos of cats. In that case, the algorithm will do a classification, analyze the patterns between the images and then be able to analyze the cat in any visual data.
Visual processing is the underlying function of this technology. It would be appropriate to answer the question of how a computer perceives and processes visual data. Machines perceive visual data with "pixels". They then assign some color values to these pixels. These color values are given in the form of numbers. These numbers are the way the algorithm sees the image. Then algorithms can perceive the image by processing the connections between pixels. We said that the different colors in the images are numbered as different pixels.
These colors are usually done according to the shades of the three selected primary colors (Red, Green, and Blue) or in black/white. The image you present to the algorithm is your "input ."The analyses or decisions that the algorithm will make after processing the image will be your "output ."If you want to get an accurate output, you need to provide the algorithm with accurate and high-quality "inputs."
Computer Vision With Deep Learning Architecture
The need for computer and machine vision is increasing day by day. For this reason, the technologies for this need are also developing at a similar pace and, more importantly, diversifying. It is up to you to choose which of these is most suitable for you. Computer vision can perform many different operations, and some of these are enabled by deep learning architectures.
First of all, we can give very brief and basic information about what Deep Learning is. Deep Learning is a subset of Machine Learning. The Deep Learning algorithm enables machines to learn on their own with neural systems that mimic the human brain with three or more layers. Here are some deep learning-based computer vision functions:
Unlike object classification, visual classification is the classification/labeling of the image itself. Deep learning algorithms can classify your preferred images by learning from similar image samples.
Object detection is a very important function. To perform this function, the algorithm uses "localization." It can detect the selected object among many objects on the image. This function is more complex than visual classification. For detailed information about object classification, please click on this article.
As the name suggests, this function is used to separate different objects. Different objects on the image or video are detected and located.
There are many other computer vision functions, and the functions mentioned above are the functions realized on the basis of deep learning architectures.
Where To Use Computer Vision
We talked about computer vision, machine vision, and artificial intelligence. We also looked at the history of these applications and how they basically work. So, the crucial question is, "Where is computer vision used?" Here are detailed examples of the uses of computer vision.
One of the most important and popular areas where computer vision is used is the automotive industry. Although self-driving cars are a sign of "the future is here" for many of us, they are becoming increasingly common technologies. The ability of vehicles to see the road, locate other vehicles and pedestrians on the road, and proceed accordingly depends on computer vision.
Self-driving vehicles are equipped with cameras, and the images obtained by these cameras are simultaneously processed by a visual recognition algorithm. Although this technology is working today, the difficult part is that training the algorithms is quite complex.
Especially during the Covid-19 pandemic, computer vision technologies were frequently used by health personnel. For example, algorithms that detect suspicious areas in the images obtained from the lungs as a result of scans have both greatly facilitated diagnosis and provided a lot of data that can be used later for training and treatment development.
With machine learning artificial intelligence, a lot of data can be collected, stored, and processed. This data can be obtained as images. For example, with a drone that you will fly over a field, you will get a lot of visual data. The processing of this data can be carried out with computer vision. In this way, information such as soil control, plant health, and temperature can be continuously updated.
It is appropriate to say that machine learning algorithms have become saviors in the field of agriculture. For example, by analyzing agricultural images, machine learning algorithms can detect diseases in plants and ensure that the necessary areas are sprayed. They usually make this determination based on color difference.
As strange as it may sound, the retail sector is one of the most effective uses of computer vision. For example, in addition to security camera images, these algorithms can be used to improve the experience by detecting the mood of the customer. At the same time, there are specialized algorithms to determine how long customers' attention spans are and which direction they are looking. All of this is based on the interests and needs of your customers.
Computer vision can take on many roles. As we mentioned before, there are many operations that you can perform using computer vision, such as object detection, object classification, and visual classification. For example, you can count people, analyze demographic, make use of emotion recognition features, make use of face recognition features, blur faces for data privacy, read barcodes and do content moderation.
Although all of these sound quite difficult, they all appear as technologies that are used quite effectively. Businesses that aim further are aware of how important it is to benefit from technology. For this reason, it is also very important to use technologies such as artificial intelligence technologies, computer vision, and machine vision. Creating algorithms for these technologies or learning algorithm literacy can be quite difficult, time-consuming, and costly. However, there are ways to get acquainted with these technologies in the most economical and fastest way possible.
Cameralyze, for example, allows you to use all these computer vision features on a web-based basis at a very low cost. Cameralyze offers a user-friendly interface and includes hundreds of ai components.It is possible to find all the functions mentioned above on the platform. For example, if you want to benefit from face detection technology, click here, upload the visual data you want to be processed and leave it. Cameralyze will do everything for you quickly! You can click here to start your free trial period.