Computer vision (CV) is the subcategory of artificial intelligence (AI) that focuses on building and using digital systems to process, analyze and interpret visual data. The goal of computer vision is to enable computing devices to correctly identify an object or person in a digital image and take appropriate action.
Computer vision uses convolutional neural networks (CNNs) to processes visual data at the pixel level and deep learning recurrent neural network (RNNs) to understand how one pixel relates to another.
Uses for computer vision include:
Biometric access management -- CV plays an important role in both facial and iris recognition.
Industrial robots and self-driving cars -- CV allows robots and autonomous vehicles to avoid collisions and navigate safely.
Digital diagnostics -- CV can be used in tandem with other types of artificial intelligence programming to automate the analysis of X-rays and MRIs.
Augmented reality -- CV allows mixed reality programming to know where a virtual object should be placed.
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Computer vision enables computers to see, identify and process images in the same way that human vision does, and then provide appropriate output. It is like imparting human intelligence and instincts to a computer. The computer must interpret what it sees, and then perform appropriate analysis or act accordingly.
When cars are equipped with computer vision, for example, they are able to identify and distinguish objects on and around the road such as traffic lights, pedestrians and road signs and act accordingly. When a human who is driving a car sees someone suddenly move into the path of the car, the driver must react instantly. Computer vision is what allows the vehicle to complete this type of complex task autonomously.
Computer vision's aim is to enable computers to perform the same kind of tasks as humans with the same efficiency.