The rising popularity of computer vision is due to the fact that it has been extremely effective in enhancing operations when applied to various sectors. Image acquisition, processing, and understanding are the three basic processes involved in computer vision and the technology has boosted the understanding of data analytics tremendously.
Today, this technology is powered by deep learning algorithms that utilize a special type of neural network to comprehend images, known as a convolutional neural network. The technology has found success in the retail, healthcare, financial, manufacturing, agricultural, and even educational sectors, and it is only expected to grow in the next decade with more technological advancements taking place every day.
A market set for growth
The market for computer vision is expected to be $24.03 billion by 2027 compared to $13.75 billion in 2019, representing a CAGR of 7.8% between 2020 and 2027. More functions will be performed using computer vision than what is currently being used thanks to technological developments.
Numerous advances to be experienced in the next decade
With regard to analytics infrastructure, computer vision is going to be a commodity component with distributed analytics as well as databases services. All devices will be expected to have application specific analytics & intelligence within the operation of the Internet of All Things.
The future systems are expected to consist of application-specific mixtures of GPUs, NCs, IO, CPUs, and sensor processors. The revenue pulled in will be from services while the technology itself will be close to a zero cost commodity.
With higher on-chip processing power, the accuracy of imaging devices will be a lot more. While there aren’t changes expected in processing algorithms, some feature descriptors as well as learning architectures, will be standardized so that a generic NC platform can be enabled for innovation that is application specific.
Computer vision technology will advance from basic deep learning models to a combination of deep learning and wide multivariate learning. This will result in feature descriptor models as well as comprehensive training protocols. Labeled samples in an extensive database will permit access to any kind of image or other data like text, audio, financial, or even details about a place, person, or a thing. Most mobile devices will get NCs connected to remote analytics for use in various applications. Neural computers will be able to assess a lot more and pick up on tone, intention, emotion, assumptions, and more for use in different sectors. Future predictions can also be made based on analyses of such data.