Computer Vision Solving Real-World Problems with Digital Visuals

Leveling the bars of human vision, the interdisciplinary field of AI is making remarkable disruptions in the business world. Dealing with how the machine can be made to attain high-level understanding from digital visuals, computer vision actually aided the spark of AI revolution. If we observe from the perspective of engineering, the technology automates the task that human visual structure can do.

After the ignition of the AI revolution in 2012, researchers and scientists employed a deep neural network to radically improve the capability of machines to identify and categorize objects in an image. Post that neural networks have outshined even human capabilities in varied areas.

More and more organizations are making hefty investments in technology's research and development work. Companies including IBM and Pinterest deploying innovative use cases in computer vision applications. These applications include security cases – detecting human face with accuracy from a distance even if the person is driving a car, in e-commerce sector where one can find products of their choice just uploading an image of it.

Computer Vision has surely become a fast-growing trend driving the potential growth of companies deploying it. The application of computer vision can be witnessed in several places – enhancing search engines, offline retail engaging with in-store buyers and even in smart fridges.

Computer Vision-driven facial recognition has paved ways for drones and video surveillance, personalized in-store advertising, connecting cars to reality, improvement in the medical sector and others.

With all this greatness also comes the drawback of such marvelous technologies – Computer vision underpinned with deep learning has fame for making black box decision. This compels organizations to employ explainable AI to make sure that machines do not execute uncontrollably. After all safety and privacy is also an important aspect to bring into consideration.