What is Computer Vision? 8 Questions You Need to Ask

Computer vision in AI (artificial intelligence) is becoming increasingly popular for use by large-scale companies to solve problems that require vision similar to the human eye. This article will answer frequently asked questions about computer vision (not technical).

What’s the Definition of Computer Vision?

Computer vision is a category within AI that trains computers to “see” and understand the visual world around them. The technology utilizes deep learning to allow computers to classify objects or analyze their surroundings. Using computer vision, computers can take in an image of the environment around them and process that image. Analytics such as objects classified within an image, size of objects, and relative distance between them are returned to the user.

Where Does the Technology Come From?

Computer vision came to light in the 1960’s, where computer scientists tried to mimic human eyesight using computing mechanics. As AI became more prominent in later years, this foundation was used to develop more sophisticated and modern techniques. Such techniques include convolutional neural networks.

When Did Computer Vision Rise to Popularity?

Despite computer vision being developed at such an early time, it only rose to popularity in the last ten years. When the technology became more easily accessible by programmers of all experience levels, it started being used in a variety of applications. AI vision has been implemented in thousands of programs since 2010. Now, many apps, websites, and programs of all backgrounds have used AI in some way or another.

Where Is Computer Vision Applied and What For?

Visual AI has been used anywhere from convenience stores to the tracking the health of crops and livestock. As an application of AI that allows computers to recognize and mark pictures, AI vision is useful in many companies. Any use that requires human eyesight can theoretically be solved using the technology.

What’s the Difference to Image Processing?

Digital image processing is a series of operations performed on an image to receive an output (such as an enhanced version or relevant information extracted from it). Depending on the context and obstacle at hand, the image can undergo various transformations such as “smoothing”, “sharpening”, “contrasting”, etc. Computer vision also takes in an input image. However, the goal is to perform an analysis or infer something about it, and imitate human vision via algorithms.

Why Is Computer Vision Challenging to Implement?

Rather than discern and process the world in images and objects (like humans do), machines see through numbers that represent individual pixels. Given the plethora of data that can be pulled from an image, vision AI needs to learn how to process all of it in order to perform complex visual tasks. To respond to different conditions as a human would, systems that implement computer vision need immense, various amounts of data. For example, in order for an automated vehicle to drive safely, it would need to understand the typical behavior of a cyclist, pedestrian, etc. and act appropriately; however, this behavior can vary depending on the individual.

What Are Common Tasks That AI Vision Systems Can Be Used For?

Computer vision can emulate basic human tasks such as face identification, object classification, etc. More complex applications stemming from these basic tasks include detecting infrastructure faults, product maintenance, surveillance, agricultural solutions, routine diagnostics in healthcare, and more. The tech is meant to let machines “see” like a human, but implement that input with advanced computing power.

What Are Some Real-Life Examples of Computer Vision in Our Daily Lives?

Vision systems can be seen in retail security, automated vehicles, healthcare, agriculture, banking, and industrial technologies. For example, some video cameras placed in stores can detect when an object has been taken and replaced from the shelf. Customers can be tracked to ensure everyone exits with the right bill. Tesla’s automated cars use 360-degree cameras and ultrasonic sensors to detect both hard and soft objects through rain, fog, dust, etc. Computer vision can be used in automated vehicles to stop car and truck collisions as well.

What’s Next?

Computer vision is an imperative aspect of companies using AI today. If you enjoyed this article, we suggest you read the article “2 Reasons Computer Vision Projects Fail” next for common mistakes programmers make before using computer vision.

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