Computer vision technology appeared in the 1950s and has gone a long way since that. Today, computer vision is used in many industries and continues to capture more and more popularity. With the professional support of AI scientists and software developers, every company can introduce automation and increase it’s efficiency via AI models.
Computer vision is called CV or AI vision.
The terms machine vision and computer vision are often used interchangeably, although these are different processes. However, together they perceive visual recognition and strengthen the business strategies of artificial intelligence as autonomous technology.
How does computer vision work?
Modern AI vision algorithms are based on pattern recognition. First, computers process thousands of images with tagged objects on them and learn to identify patterns. Convolutional neural networks then collect all parts of the visual images like a puzzle and study to classify different objects.
Computer vision in the automotive industry
Recently, the automotive industry has focused on improving the technology of self-driving cars using CV methods as priorities.
Tesla cars track the surrounding area using all-round cameras to provide an advanced system for helping the driver on autopilot and detecting and classifying objects. Here, path planning, picture monitoring, and driving behavior rely on computer vision.
Image Recognition in Security
Some of the most prominent examples of computer vision technology are AI-based facial detection and recognition, which identify crimes by tracking devices and carrying out security missions.
Machine vision and deep learning also contribute to facial recognition. The CV algorithms combination helps detect a face and send it to the system for further analysis.
Computer Vision in Retail
Throughout the coronavirus pandemic and economic recovery, computer vision applications in retail have become widespread.
For example, retail stores can use CV technologies to check customer activity. It helps to retain customers by welcoming regular buyers and identifying the dissatisfied.
Retail computer vision functionality also includes inventory tracking and evaluation of product placement strategies.
One of the e-commerce giants, Amazon, is also rolling out visual search technology to display well-optimized content.
Imaging Techniques in Healthcare
Medical software systems are based on image classification and pattern detection. They help doctors diagnose dangerous diseases.
CV methods have achieved a significant breakthrough in radiology, pathology, and ophthalmology. Underlying the spread of AI vision is the nature of diagnostic tasks associated with visual pattern recognition.
For example, Microsoft InnerEye democratizes AI by using 3D X-ray images and helps improve biological tissue diagnostics for all healthcare settings.
Monitoring of processes in the education system
Another example of computer vision in AI is EdTech. It allows teachers to organize the learning process without hindrance and outside the box. CV-enabled webcams check students by monitoring their behavior and eye movements. EdTech software includes UAuto and verifies identity through multifactor authentication.
Thus, educators personalize the educational process and identify disinterested students. AI also focuses on knowledge acquisition, assessment systems, attendance monitoring, and school logistics support.
Tracking systems in sports
In various games, camera-based tracking systems can detect moving balls, and AI-enabled software determines the position of all players at a specific point in time.
SentioScope for player tracking processes the game in real time and downloads data to analytical platforms in the cloud.
Know-how in agriculture
Today, computer vision technologies can improve agricultural productivity by introducing the CV and AI models. Some known systems and applications of machine vision include:
- Monitoring of crops by drones
- Smart systems for classifying and sorting crops
- Automatic spraying of pesticides
- Yield monitoring
- Weather records
- Smart Farming
- Field Security
- Forest information
Brazilian startup Cromai is also contributing to the agricultural sector by creating AI-based solutions that scan crop color, shape, and texture.
Human pose estimation in games
The human pose scoring model processes visual content and estimates human posture in 2D or 3D format. It has found applications in robotics, console, physical therapy, and fitness apps. For example, Xbox, possessing the ability to AI, allows the system to determine the position of the human skeleton joints in three-dimensional space and recognize the movements of these points.
Media and entertainment in the CV spotlight
The development of computer vision and immersive technologies translate digital media into a more interactive spectrum. Today, viewers can enjoy animation, moving graphics, digital credits, and other dynamic elements. This level of interaction is also possible thanks to controllers and smart glasses.
Google Glass is a striking example of interactive equipment and facial recognition technology. The Glass optical head display shows information directly in the user’s field of view. Hardware responds to head and face movements, allowing users to turn pages with a simple head tilt.
Manufacturing Defect Detection
AI inspection systems are widely used in research laboratories and warehouses. For example, preventive maintenance uses monitoring systems to detect deformations and prevent breakdowns. And artificial intelligence systems help control packaging and product quality.
AI Vision performs automated assembly and product management processes, especially in assembly lines with fragile objects or parts. For example, Tesla was the first to fully implement these processes.
Distribution of AI in the transport sector
Intelligent CV-based traffic violation detection and handling technologies help law enforcement authorities reduce dangerous road behavior.
For example, Hikvision checkpoint systems record traffic violations: the camera takes a picture of the car and its differentiators every time a car passes through an equipped checkpoint.
Traffic flow analysis is another area of AI application. It is complemented by intelligent transport systems that greatly contribute to the development of smart cities.
It took about 80 years for specialists to achieve today’s level of computer vision.
One of the restraining factors in the development of this field of knowledge remains the complexity of a full understanding of biological vision in the dynamic world.
Breakthroughs in deep learning and artificial intelligence demonstrate that this technology increases the ease of use and most areas of life and economic efficiency.