Computer Vision is one of the Machine Learning applications that we already use daily, sometimes without even knowing it.
For example, Google’s algorithms scan maps to extract valuable data and identify the names of streets, businesses, or corporate offices.
For its part, Facebook uses computer vision to identify people in photos. On social networks, it also automatically detects inappropriate content for immediate censorship. There are several companies doing computer vision development, which you can contact if you want to apply this technology in your field. Let’s look at the different use cases of computer vision.
Autonomous Cars
Tech companies are far from the only ones turning to this technology. Thus, the automaker Ford is using Computer Vision to develop its future autonomous vehicles. These are based on the real-time analysis of numerous video streams captured by the car and its cameras.
The same goes for all unmanned car systems like those from Tesla or Nvidia. The cameras on these vehicles capture video from different angles and feed the computer vision software.
The latter processes the images in real-time to identify road edges, read traffic signs, detect other cars, objects, and pedestrians. Thus, the vehicle can drive on motorways and even in built-up areas, avoid obstacles, and lead passengers to their destination.
Health
In the field of health, Computer Vision is also experiencing a real boom. Most diagnoses are based on image processing: reading X-rays, MRI scans, etc.
Google has partnered with medical research teams to automate the analysis of these images using Deep Learning. Significant progress has been made in this area. Computer Vision AIs are proving to be more efficient than humans at detecting certain diseases such as diabetic retinopathy or various cancers.
Sports
In the field of sport, computer vision provides invaluable assistance. For example, Major League Baseball uses AI to track the ball accurately. Likewise, London-based startup Hawk-Eye is deploying its ball tracking system in more than 20 sports such as basketball, tennis, and football.
Facial Recognition
Another technology based on Computer Vision is facial recognition. Thanks to AI, cameras can distinguish and recognize faces. The algorithms detect facial features in images and compare them with databases of many faces.
This technology is used on consumer devices like smartphones to authenticate the user. Social networks also use it to detect and identify people in photos. For their part, the authorities use it to identify criminals in video streams.
Virtual and Augmented Reality
New virtual and augmented reality technologies are also based on Computer Vision. It is this which allows augmented reality glasses to detect objects in the real world and to scan the environment to be able to dispose of virtual objects.
For example, algorithms can allow AR applications to detect flat surfaces like tables, walls, or floors. This makes it possible to measure the depth and dimensions of the real environment to integrate virtual elements into it.
The Limitations and Problems of Computer Vision
Computer vision still has limitations. In reality, the algorithms are content to associate pixels. They don’t really “understand” the content of images like the human brain.
For a good reason, understanding the relationships between people and objects in images requires common sense and knowledge of the context. This is precisely why the algorithms responsible for moderating content on social media cannot tell the difference between pornography and more candid nudity like breastfeeding photos or Renaissance paintings.
While humans use their knowledge of the real world to decipher unfamiliar situations, computers cannot. They still need precise instructions, and if something unfamiliar comes their way, the algorithms get out of hand. For example, an autonomous vehicle will be taken by surprise in front of an emergency vehicle parked incongruously.
Even training an AI with all the data available, it is impossible to prepare it for all possible situations. The only way to overcome this limitation would be to create a general artificial intelligence, namely, an AI truly similar to the human brain.