While computers seem like modern technology, they have been in the making for thousands of years. Ancient civilizations used tools to help them solve mathematical problems, and this basic idea was built on by 20th-century scientists to produce the world’s first computer systems.
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Konrad Zuse is the father of computer research. His Z3 was built in 1935 and was the first functional program-controlled automatic computer. It was the blueprint for all modern computers. Important developments continued throughout the 20th century, with the world’s first home computer being released in the 1960s and Steve Jobs’ and Steven Wozniak’s Apple 1 completed in 1976. Since then, computer technology has come a long way. Devices have become more intelligent, faster, and come in many different forms other than the classic desktop computer (think of tablets, smartphones, smartwatches). The capabilities of the modern computer and applications of computer technology continue to expand and show no signs of stopping.
What is Real-Time Image Recognition and How is it Used in Computers?
Image recognition is a complex task. The human brain dedicates an entire section to do this (the visual cortex). When we look at a scene, our brains pick up on the objects within the field of view and recognize them unconsciously. The process seems effortless, but a lot of neuronal activity is required to perform these computations.
When we talk about image recognition in computers, the concept is the same. It refers to processes that recognize distinct objects in the field of view, for example, understanding that a person is a separate entity to the chair they are sitting on and the coffee cup they are holding.
While natural to humans, image recognition is a complex task to program in computers. Over the years, the computer vision field has been challenged as accurate and intuitive visual recognition systems have been developed. The common goal of the sector is to establish technology that can classify detected objects into distinct categories.
Over recent years, developments in machine learning have helped to further the research in computer vision. Deep learning image recognition systems are now considered to be the most advanced and capable systems in terms of performance and flexibility.
Recent breakthroughs in image recognition have been made possible by innovative combinations of deep learning and artificial intelligence (AI) hardware. Systems have been developed to allow them to execute object recognition in real-time, which means they can identify and recognize objects in a live stream. Now, image classification and face recognition algorithms have been proven to perform better than humans at real-time object recognition.
Current and Future Industry Developments
AI has made significant advances over the last few decades and as it has advanced, its uses across fields of science have grown. Recent developments in image recognition have included the adoption of AI for handwriting recognition, image captioning, and autonomous vehicle recognition.
The Internet of Things (IoT) is another branch of technology that is influencing real-time image recognition. The IoT refers to the network of connected devices that has become commonplace in our day-to-day lives. Laptops, phones, alarm systems, wireless doorbells, smart home devices such as smart lights and heating systems are an ecosystem of technology that is part of the IoT.
Industries such as farming rely on the IoT further with the integration of sensor technology into its processes. The integration of AI alongside the IoT has led to a new category of AIoT applications that perform intelligent real-time image recognition.
Recent innovations include image processing in smart factories to monitor machinery, abnormality identification in medical images such as MRI scans, and automatic real-time scanning and recognition of license plates to help identify stolen cars.
How Will Real-Time Image Recognition Help Shape the Future of Quantum Computers?
Real-time image recognition will go hand in hand with the birth of quantum computers, a new generation of computers that stores data in quantum bits rather than traditional bits (0s and 1s of binary code). Quantum bits can hold information in multiple states at the same time, making quantum computers incredibly more powerful with the potential to overcome some of modern computing’s biggest limitations.
Quantum computing lends itself to solving the current challenges of real-time image recognition. It can use generative models, for example, in the scenario where computers need to recognize a face for security purposes but the algorithms have only been provided side profile shots. This is not a problem for a quantum computer as it can generate these images and enhance their quality. This use of quantum computers in facilitating the development of real-time image recognition will no doubt help the field advance.
References and Further Reading
Chen, M. and Wu, H., 2021. Real-time intelligent image processing for the internet of things. Journal of Real-Time Image Processing, 18(4), pp.997-998. https://link.springer.com/article/10.1007/s11554-021-01149-0
Ranjani, J., 2019. Quantum Image Processing and Its Applications. Handbook of Multimedia Information Security: Techniques and Applications, pp.395-411. https://link.springer.com/chapter/10.1007/978-3-030-15887-3_18
Wang, Z., Xu, M. and Zhang, Y., 2021. Review of Quantum Image Processing. Archives of Computational Methods in Engineering,. https://link.springer.com/article/10.1007/s11831-021-09599-2
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