The Present and Future of Computer Vision

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Computer vision, or the ability of artificially intelligent systems to “see” like humans, has been a subject of increasing interest and rigorous research for decades now. As a way of emulating the human visual system, the research in the field of computer vision purports to develop machines that can automate tasks that require visual cognition. However, the process of deciphering images, due to the significantly greater amount of multi-dimensional data that needs analysis, is much more complex than understanding other forms of binary information. This makes developing AI systems that can recognize visual data more complicated.

But, the use of deep learning and artificial neural networks is making computer vision more capable of replicating human vision. In fact, computer vision is becoming more adept at identifying patterns from images than the human visual cognitive system. For instance, in the field of healthcare, computer vision-based technology has said to have exceeded the pattern recognition capabilities of human physicians. Researchers have tested an AI that can detect neurological illnesses by reading CT scan images faster than radiologists read.

With similarly astounding feats by AI with computer vision technology becoming increasingly common in different industries, the future of computer vision appears to be full of promise and unimaginable outcomes. Read on to know the state of computer vision technology today and where it is heading in the future.

The Present State of Computer Vision Technology

Computer vision technology of today is powered by deep learning algorithms that use a special kind of neural networks, called convolutional neural network (CNN), to make sense of images. These neural networks are trained using thousands of sample images, which help the algorithm, understand and break down everything that’s contained in an image. These neural networks scan images pixel by pixel, to identify patterns and “memorize” them. It also memorizes the ideal output that it should provide for each input image (in case of supervised learning) or classifies components of images by scanning characteristics such as contours and colors. This memory is then used by the systems as the reference while scanning more images. And, with every iteration, the AI system becomes better at providing the right output. Following are a few areas in which computer vision technology is being used or tested:


 

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Image Captioning

Image captioning is probably the application of computer vision we all might be the most familiar. Social media platforms such as Facebook and Instagram use deep learning algorithms to identify the elements of images that are posted by users. These algorithms have become increasingly proficient at not only distinguishing humans from animals and inanimate objects but also identifying individual humans based on their facial features. Image captioning technology can be used in any area where textual information needs to be extracted from images.

Facial Recognition and Biometrics

Facial recognition and biometric scanning systems also use computer vision technology to identify individuals for security purposes. The most common example of computer vision in facial recognition is for securing smartphones. More advanced uses of facial recognition and biometrics include in residential or business security systems that use unique physiological features of individuals to verify their identity. Deep learning algorithms can identify the unique patterns in a person’s fingerprints and use it to control access to high-security areas such as high-confidentiality workplaces, such as nuclear power plants, research labs, and bank vaults.

Computer vision systems are also great at recognizing subtle differentiating patterns in peoples’ Retinas and Irises, which are much more effective as unique identifiers. These systems can be used to enhance the security of high-value assets and locations.

Self-driving Vehicles

Self-driving vehicles, in order to navigate safely through the streets, must be able to identify the obstacles, signposts, other vehicles, and any person that may come in the way. For this purpose, these cars are equipped with multiple tools, such as LiDAR and ultrasonic sensors. They also use cameras covering their entire perimeter that provide computer vision to safely orient themselves with respect to their surroundings. While other technologies might help self-driving vehicles to recognize and avoid obstacles, computer vision can help them to read road signs and follow traffic rules for maximum safety. Computer vision can also help in making critical on-road decisions such as giving way to ambulances and fire engines.

Medical Diagnosis

Computer vision, in addition to being adept at recognizing elements and objects from digital images as accurately as humans, can also identify patterns that may be missed by the human visual system. For instance, researchers have developed an AI that uses computer vision to identify cancer tumors from CT scan images to diagnose lung cancer better than human radiologists. These applications can help prevent the late detection of cancer and can help patients to receive timely treatment. Computer vision can be used to visually identify other forms of cancer and other diseases with greater accuracy than humans.

Law and Order

Computer vision can be used to scan live or recorded surveillance footage to help law and security officials with vital information. For instance, computer vision can be used to scan the live footage from a public area to identify harmful objects such as guns or to identify suspicious behavioral or movement patterns that may foreshadow any illicit action by individuals, based on historical data. With further development, computer vision can also be used to scan crowds of people to highlight the presence of any persons of interest or wanted individuals to the concerned authorities. Thus, using computer vision can help in expediting the apprehension of people of interest and preventing crimes.

Manufacturing

The manufacturing sector is among the sectors that have seen the most extensive use of automation and robotics. As more and more manufacturing units transition towards fully automated manufacturing, they will need to use more intelligent systems to monitor industrial processes and outcomes. While technologies like the Internet of Things (IoT) are revolutionizing manufacturing sector and making processes more autonomous, computer vision can further help in improving them. For instance, computer vision can be used for inspecting manufactured products for defects and nonconformities. Thus, it can eliminate the need for human inspection on the production line.

The Future of Computer Vision Technology

With further research on and refinement of the technology, the future of computer vision will see it perform a broader range of functions. Not only will computer vision technologies be easier to train but also be able to discern more from images than they do now. This can also be used in conjunction with other technologies or other subsets of AI to build more potent applications. For instance, image-captioning applications can be combined with natural language generation (NLG) to interpret the objects in the surroundings for visually challenged people. Computer vision will also play a vital role in the development of artificial general intelligence (AGI) and artificial superintelligence (ASI) by giving them the ability to process information as well as or even better than the human visual system.

Considering the capabilities of present-day computer vision, it might be hard to believe that there are more benefits and applications of the technology that remain unexplored. The future of computer vision will pave the way for artificial intelligence systems that are as human as us. However, before doing so, there are a few challenges that must be overcome, the biggest of them being the demystification of the black box of AI. That's because just like other deep learning applications, computer vision while being functionally effective is undecipherable when it comes to its inner workings.


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Naveen Joshi is Founder and CEO of Allerin, which develops engineering and technology solutions focused on optimal customer experiences. Naveen works in AI, Big Data, IoT and Blockchain. An influencer with a half a million followers, he is a highly seasoned professional with more than 20 years of comprehensive experience in customizing open source products for cost optimizations of large scale IT deployment.