There are so many things happening in the world of computer technology and artificial intelligence (AI) that it is a challenge to know where to start. In terms of applications, computer vision is creating a lot of buzz. Technically, computer vision is defined as the technology allowing the digital environment to act together with the real world. It is a branch of artificial intelligence that tries to reproduce human vision’s capabilities. Computer vision trains computers to interpret and understand the visual environment. It uses digital images from videos and cameras, and deep learning models to identify and classify objects accurately and provide a reaction to the image it sees.
Perfecting visual recognition technologies – and, by extension, building and deploying computer vision pipelines – represents just one of the many facets involved in the development of AI-assisted technologies at the enterprise levels. Companies like Dataloop.ai represent the forerunners of these methodologies and technologies which will – and, in many cases, already – underpin a long list of areas in business, and life.
Increase in adoption of computer vision
The capabilities of visual recognition systems have increased recently, thanks to advancements in technology and developments in deep learning approaches. Thus, many companies have already adopted computer vision. Here are some of the applications of computer vision across varied industrial sectors.
Estimation of human pose
One of the interesting applications of computer vision is human pose estimation. It is a technique to guess the pose of an object or person present in the video or image. It uses the human pose skeleton, a group of coordinates defining the person’s pose. The pose estimation is carried out by identifying, locating, and tracking the key point of the human pose skeleton.
You can use human pose estimation in the following:
- Robot training
- Augmented reality experiences
- Activity recognition for real-time surveillance system or sports analysis
- Gaming and animation
Image transformation with GANs
If you are familiar with FaceApp, you can understand the mechanics of image manipulation to transform the input image with the use of filters, such as gender swap or aging tools. This is part of computer vision, employing generative adversarial networks or GANs.
The training of GANs includes two neural nets that play against each other to generate new data. It can be used for semi-supervised and supervised learning. GANs can be used for:
- Image to image translation in photo in-painting and style transfer
- Creating an image with very high resolution
- Text to image generation
- Image editing
- Photo translation from semantic image
Development of social distancing tools
Even with the availability of vaccines, the threat of the pandemic is still around us, and social distancing is still one of the most effective ways to reduce transmission, along with wearing face masks and using hand sanitizers frequently.
Computer vision can be used to track people in a particular area to know if they are following the social distancing requirements. You can develop a social distancing tool to detect objects and track them in real-time.
Convert 2D images into 3D models
You can apply computer vision to gaming and animation, self-driving cars, robotics, surgical operations, and medical diagnosis. Medical doctors find it easier to understand and interpret medical images when they are converted into 3D interactive models.
If a basic computer looks at an image it sees nothing. But with computer vision, the computer can identify and recognize the faces, ages, and even ethnicities of everyone.