Computer vision is the process through which a device takes a photo or video, and then the computer analyzes the media to understand and interpret it. Through careful analysis, computer vision can determine traffic levels, expiration dates on food, and weather risks.
Computer vision is relatively new. Soon, though, it will become ubiquitous as experts hone the technology and expand its applications.
Computer vision plays an invaluable role in enhancing machine learning. Machine learning is the study of algorithms and statistical models in various processes. With these algorithms and models, computer systems can complete tasks without explicit instructions. Machine learning plays a vital role in everything from Barbie dolls to agriculture and financial services.
Computer vision and machine learning share a symbiotic relationship. While computer vision has broadened the scope of machine learning, machine learning helps computer vision systems recognize and track objects.
Below are three examples of the relationship that machine learning and computer vision share:
Autonomous Vehicles
Self-driving cars use hundreds of machine learning algorithms. These algorithms help vehicles understand the meaning of yield signs and determine the danger of a scenario. But with the aid of computer vision, this process becomes much easier. Computer vision can recognize yield signs, pedestrians crossing the street on a green light, and sudden risks. Thus, with computer vision, the road becomes safer for drivers and pedestrians.
Manufacturing
Without computer vision systems, machine learning can play a slim but vital role in a factory assembly line. But with computer vision, computers can recognize hundreds of objects and know exactly what to do with each of them.
Machine learning and computer vision systems are especially helpful for replacing humans in processes that don’t require too much attention or that take a long time.
Retail
With the emergence of online shopping, many companies are experimenting with dark stores. Dark stores are essentially warehouses that stock consumer goods and distribute them without any customers in the store. In this model, consumers place orders online, and people or robots find the orders and supply them to distributors or directly to the customer.
With machine learning and computer vision, robots can recognize, locate, and package hundreds of objects in a store. This process proves more affordable and efficient for companies and quicker for customers.
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