The global market for AI in computer vision will reach $73.7 billion by 2027, is one of the conclusions of the “AI in Computer Vision Market by Technology, Solutions, Use Cases, Deployment Model and Industry Verticals 2022 – 2027” report by ResearchAndMarkets.com
In addition to the prediction that the global market for AI in computer vision will reach $73.7 billion, the researchers expect global reinforcement learning in computer vision to reach $34.7 billion. Global 2D and 3D machine vision will reach $3.4 billion and $7.4 billion respectively, and global AI in computer vision by unit volume expansion will grow at 37.8% CAGR through 2027. The global market for cameras with greater than 125 frame rate per second will exceed $10 billion and the Asia Pacific software market in support of AI in computer vision will reach $11.8 billion by 2027 with 33.7% CAGR.
The report assesses the application of AI in computer vision systems used in conjunction with connected devices, hardware components, embedded software, AI platforms, and analytics. The report analyzes machine learning models and APIs used in computer vision systems along with the application of neural networks in AI analytics systems.
This research also evaluates the causal relationship of computer vision systems with IoT, Edge computing, and connected machines along with core hardware and software technology. The report also analyzes the relation of emotion AI with computer vision systems along with the market factors.
Simulate human visual system
Computer vision systems are dedicated to simulate the human visual system while analyzing the information extracted from photos and videos. They do this by way of mathematical operations in conjunction with signal processing systems to process both digital and analog images. These systems leverage both two dimensional and three-dimensional processes.
AI represents the ability to organize information and create outcomes in learning, decision-making, and problem-solving using a computer-enabled robotic system in the same way a human brain does. The integration of AI and computer vision systems enhance the accuracy of object identification, classification, and analysis of information.
Through leveraging AI, computer vision systems provide a robotic system in which vision sensing capabilities provide information about the environment. One of the best examples of this in practice is autonomous vehicles, which rely on computer vision and AI-based decision making for safe travel.