The field of computer vision and everything related to it can be called relatively new, diverse, and rapidly developing.
Computer vision is the technology of creating machines that can detect, track and classify objects.
According to TAdviser research, from 2018 to 2023, the domestic market for Computer Vision technology will increase five times. The largest share is occupied by video surveillance and security solutions – 32%, manufacturing – 17%, healthcare – 14%, and trade – 10%.
Reasons why does the world need Computer Vision?
Video surveillance is an essential part of physical security. Therefore, people created IVS (intelligent video surveillance) systems based on deep learning. It helps to recognize unusual events or objects in video surveillance frames.
The Image processing platform implements such features as: face, motion, or other anomaly detection. If any unusual phenomena are detected, warnings are issued and the object is highlighted with a frame. So it can prevent any danger.
Recognition and video analytics systems are used to analyze the attendance of retail outlets, the movement of customers, and the average time spent in line. This allows us to optimize the staff’s work schedule and make the stay in the store more comfortable and the service faster.
“Supplement” to human capabilities
Computer vision is able to see things that people might not see. This is especially true in healthcare (e.g. X-ray analysis) and manufacturing (defects detection). Machines capture more details and use a lot more data in such research. According to psychologists, the average time a person’s attention is held on one object does not exceed 14 minutes. The main advantage of computer vision over human vision is concentrating on an infinite number of objects.
Reduced time for routine tasks
Instead of manually everyday tasks fulfillment, we can use computer vision to enhance our workflow and reduce time. For example, the sales representative can easily take a shelf picture to check the goods’ availability, arrangement, and relevance of price tags. Such systems can compare it with the planogram and give recommendations: what product is missing, what is in the wrong place or if someone mixed up the price tags.
The mortality rate due to car accidents is 2.2% of the total number of deaths globally. The most widespread reason is the “human factor”. Cross-Traffic Alert systems help prevent accidents when the driver does not notice the vehicle moving in the cross direction. Such systems are usually built based on radars operating at high frequencies (20 GHz and higher). However, they are pretty expensive and can be installed in high-end cars as an option. Computer vision can greatly simplify such systems and make them widely available.
If you are interested to know more about the future of this area, then read this article, as we are going to analyze such vital issues as:
- How Does Computer Vision Work?
- How Businesses Can Benefit From Computer Vision in 2022?
- Top ways to implement Computer Vision
- Azati and Computer Vision
How Does Computer Vision Work?
In the article “Image Processing and Computer Vision”, Golan Levin talks about how Computer Vision works. Machines interpret images as a series of pixels, which has a unique set of color values. Consider an example. Below is a photograph of Abraham Lincoln, where the intensity of each pixel is indicated by one 8-bit number ranging from 0 (black) to 255 (white). The program «sees» these numbers when it gets the image. These numbers are provided as data-in to a computer vision method responsible for analysis and decision making.
Computer vision analyzes the data an unlimited number of times until it starts to find differences and, as a result: recognizes images. If you send thousands of flower photographs, the computer will first analyze them and then identify patterns that unite all these images to create a “flower” model eventually. Two technologies are used to accomplish this tricky operation: deep learning and a convolutional neural network (CNN).
Deep learning refers to an algorithmic model that helps a computer learn the context of visual information. If enough data is passed through this model, the computer will “look” at it and try to distinguish one image from another. Briefly, specific algorithms allow a machine to learn itself.
CNN helps by breaking down images into pixels, which are assigned tags. It uses labels to perform convolutions (a mathematical operation on two functions to get a third function) and tries to recognize what it “sees”. The neural network convolutions analyze the accuracy of its predictions over a series of iterations until they come true. Then it identifies the images in the same way as humans.
Let’s describe the main approaches to solving problems related to Computer Vision:
1. Contour analysis
An object’s silhouette is a curve corresponding to the border of an image. In the mentioned method, not the entire image is examined, but only its outline. So that we can significantly reduces the algorithms complexity and calculations during processing.
2. Pattern search
It is used to understand whether there is an object we need in the photo and where it is located. There are several types of pattern matching.
Here are some of them:
– Simple matching. The method consists of step-by-step scanning of the initial image with a template. At any step, the ratio level of the defined area is measured or calculated.
– Feature-based matching. This method is used when the template and the image match in terms of features and control points. The purpose is to discover pairwise relationships between a “reference” and a part of an image, using spatial relationships or features.
– Area-based matching, or correlation method. This approach works well if the patterns don’t have any noticeable common denominators with the image, since the comparison happens at the pixel level. Sometimes, this is impossible. So the solution uses eigenvalue and eigenspace. These indicators contain information important for comparing images under different lighting conditions, contour contrast, or coincidence in the position of objects.
3. Search beyond templates, matching by key points
Object detection is finding instances of objects in an image. When it is authenticated, not only the fact of presence in the picture is established, but also its location. Detection involves comparing two or more images when searching for unique objects.
4. Data Combination
Data Fusion combines information from various sources with images to obtain more accurate and valuable information. The combined analysis of data from the CV system and the complex sensors help significantly increase the value of the received information and significantly improve the application’s performance.
How Businesses Can Benefit From Computer Vision in 2022?
Like other types of Artificial Intelligence, computer vision tries to expand the capabilities of machines by imitating people’s abilities becoming the “center” of many innovations. There are many areas of our life in which this technology can become an indispensable assistant.
Computer vision can help keep workers safe. For example, video analytics systems monitor if all employees wear personal protective equipment in hazardous industries. If a person is not wearing work equipment, he receives a notification. Also, being on the central control panels, where it is important to be concentrated and not be distracted, face recognition systems monitor the specialist’s condition. If an employee is sleepy and shirks from his duties, he and the whole team receive a warning.
Computer vision undoubtedly improves the process of combating criminality, particularly against fraud. Thanks to this modern technology, our faces can become our identifiers. Nobody canceled the passport we were used to, but face authentication will provide better protection.
This subspecies of AI is also becoming an indispensable assistant to the doctor. This method analyzes medical images, such as X-rays, MRI, and ultrasound, helping to improve the accuracy of diagnosing diseases.
Without computer vision, robotic surgeons, who will soon enter many operating rooms, will not be able to “work”. Neural network algorithms also help improve the quality of X-ray and CT images, eliminating unnecessary noise and distortion. It can reduce the residence time in the apparatus and the radiation dose by up to 25%.
It is worth noting that computer vision is an essential part of developing all types of transport. It can analyze the parking lots occupancy, providing information to the urban transport system. The level of autonomy varies from fully crewless vehicles to vehicles where computer vision-based systems assist the driver or pilot in various situations.
Military applications are perhaps the largest area of computer vision. Prominent examples are detecting enemy soldiers, vehicles and can provide missiles control. A new-fangled military term such as “combat awareness” implies that various sensors, and provide a wealth of information about the battlefield that can be used to make strategic decisions. In this case, data preparation is used to reduce the complexity or increase the reliability of the information received.
Top ways to implement Computer Vision
Let’s look at several industries where computer vision has already been introduced, and such an “introduction” has been quite successful.
Retail and e-commerce
Tiller is a store navigation app based on computer vision algorithms. Thanks to this software, it is easy to find the way to the products you need.
Now let’s get into e-commerce and immediately mention an excellent service, Bodygram, which helps scan the body using the subspecies of AI discussed in this article. Neutrogena, a skincare brand, recommends trying out an app like Skin360 that helps you know your skin condition and then gives recommendations on how to care for it.
Sweetshop Lolli & Pops uses computer vision for facial recognition to identify its frequent customers and offer them products based on their tastes and preferences and goodies as a discount for loyalty.
Computer Vision in education can help university and school teachers analyze students’ moods and behavior to understand the degree of their involvement in the learning process. The Little Dragon app reads facial expressions to detect frustration or boredom and tailor learning content. Аs a result, it can help the administration tailor the school environment to students’ preferences to provide a comfortable learning environment.
In addition to surgical robots, there is also Proprio Vision, which combines computer vision with machine learning and virtual reality to create 3D visualizations for doctors in the operating room.
Moreover, we cannot fail to mention such an excellent application, Project Guideline, where computer vision technologies are also implemented. This service allows the blind to run by speaking out the prompts, text, and images surrounding the client and also provides a feature such as voice input.
Fitness and sports
The best examples are SentioScope, developed by Sentio for monitoring and evaluating football players, and SportVU 2.0 optical tracking technology, which gives football coaches a holistic view of matches.
For yoga and healthy lifestyle enthusiasts, check out Zenia, which has created a fitness app built on computer vision and machine learning, that can reportedly recognize yoga postures with 95% accuracy.
Azati has also been fortunate to work with computer vision on some of our projects.
We have developed a unique solution based on artificial intelligence and computer vision for a pharmaceutical company. The main goal was to facilitate the search and comparison of the required product with the recommendations of doctors based on questionnaires and the opinions of professionals. As a result, our company has developed a machine learning model to analyze data from medical questionnaires, find insights, and generate reports for the end-user within the pharmaceutical field. You can read more about this case here: NLP Solution For Pharmaceutical Marketing.
Also, we successfully built a prototype system that can detect defects on roads by analyzing images and videos. The goal of this project was to train computer vision to identify road imperfections, especially potholes. .
And it is impossible not to mention that Azati, together with our strategic partner DIGATEX, were the first to successfully implement CV technologies to make artificial intelligence handle a large number of documents with flexible structure and custom abbreviations. You can learn a little more by reading Cloud System for Document Digitization.
Computer vision is a powerful technology that can be combined with various applications and touch devices to realize many practical use cases.
Nowadays, computer vision applications have become ubiquitous. It’s hard to imagine where the computers’ ability to “see” what’s going on around them has not yet been exploited.
If you have any great ideas on how to apply Computer Vision technology – drop us a line, and we’ll have a chat about how Azati can help your business.