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Even just a decade or so ago, many people perceived artificial intelligence (AI) as something reserved for futuristic science fiction films. But now, AI technology has quickly become an invaluable tool for a broad range of businesses, proving useful in a variety of applications.

Among the many areas of AI gaining ground is computer vision technology, something scientists and computer engineers have been working to develop for over half a century. In this guide, we’re taking a closer look at computer vision in AI, providing an in-depth perspective on what it is, how it works, and how it’s being applied right now.

What is Computer Vision?

Computer vision is a subfield of artificial intelligence (AI) that allows computers and systems to deduce useful, meaningful data from digital images, videos, and other visual elements, using that information to provide recommendations and/or take specific actions.

Think of it this way: AI grants computers the ability to think; computer vision makes it possible for them to see, observe, and interpret.

Computer vision machine learning essentially teaches machines to be able to utilize context to understand images, tell objects apart, pinpoint what is wrong in an image, and so on and so forth – much like humans can. However, a computer vision-enabled system can perform these tasks far faster and more efficiently than a human ever could. Because the machine taps into data and algorithms to process visual elements, it can analyze thousands of processes or products in a single minute with flawless accuracy.

How Computer Vision Works

Ultimately, computer vision relies on data and lots of it. A computer vision application analyzes data repeatedly until it is capable of pinpointing distinctions and, thus, recognizing images.

There are two core technologies needed to implement computer vision:

  • Deep learning: Deep learning is a form of machine learning which utilizes various algorithmic models to allow a computer to “teach” itself about visual data and its context. These algorithms eliminate the need for manual programming, so the machine can learn to recognize an image by itself.
  • A convolutional neural network (CNN): A CNN breaks images down into pixels, which are then assigned labels or tags. These labels are used in precise mathematical operations to develop predictions about the image that the computer is “seeing.” From there, the CNN repeatedly runs these operations to check the accuracy of its predictions in numerous iterations until the predictions can be confirmed. In simpler terms, a CNN is able to fill in information about a visual image to gain a meaningful understanding.

Computer vision can be broken down into three key steps:

  1. Acquiring an image
  2. Processing the image
  3. Understanding the image

Acquiring an image

Images are collected in real time via photos, video, or 3D technology. Oftentimes, large sets of images will be acquired for analysis.

Processing the image

After being supplied with thousands of pre-identified, labeled images, deep learning models are developed. These models can then automate much of the processing stages.

Understanding the image

Finally, the computer interprets the image through identification and/or classification.

What is Computer Vision Used For?

Once a computer has established a detailed understanding of an image, it can communicate the information with a relevant AI system to take one or more actions. There are many different types of computer vision that can be used for various purposes. For example, some common techniques in computer vision include:

  • Image segmentation breaks an image down into multiple pieces, each one of which can be evaluated independently.
  • Object detection can identify a specific object contained within an image, with advanced object detection systems capable of recognizing multiple objects at a time. An object detection computer vision system might be used on a factory assembly line as a way to detect defective items or flag damaged equipment components.
    • Facial recognition is an advanced form of object detection which can recognize a specific human individual’s face in an image.
  • Object tracking follows a specific object once it is detected and is frequently utilized with real-time video feeds or sequentially captured images. For example, autonomous vehicles track objects in motion in order to avoid a collision.
  • Edge detection is utilized to locate the outer edge of a landscape or object, so the computer can more effectively process what is in the image.
  • Pattern detection is a technique that recognizes repeated visual indicators, such as colors or shapes, in a series of images.
    • Feature matching is a kind of pattern detection that classifies visuals by matching similarities between multiple images.
  • Image classification is used to organize images into categories based on specific parameters. One example of image classification is used by social media providers, who can use the tool to automatically identify and classify potentially objectionable images uploaded by users.

In simpler applications of computer vision, just one of these techniques may be applied. But in more advanced applications, such as computer vision and self-driving vehicles, multiple techniques may be used.

Who’s Using Computer Vision?

Companies across virtually all industries are partnering with AI technology providers to implement computer vision strategies that support their needs and goals.

In fact, many of us interact with computer vision technology on a daily basis. Everything from the facial recognition that unlocks your smartphone to self-driving vehicles, Google Translate, and other everyday technologies rely on computer vision in some form.

Computer vision is offering improvements in the arenas of customer experience, worker safety, productivity, and more. Companies are often able to reduce overall costs and boost the efficacy of their efforts, resulting in better outcomes for consumers across the board.

Challenges of Computer Vision

Computer vision algorithms and technology has grown by leaps and bounds in recent years, improving its accuracy rate from 50 to 99% in the span of a decade. But, like many emerging technologies, there are still some challenges related to computer vision applications.

The visual system used by humans is incredibly complex, and computer vision systems rely on a comparison basis. So, computers must undergo a significant level of training and learning work to handle tasks that would be second nature for humans. That being said, once sufficient learning has been completed, computer vision systems are infinitely more efficient overall.

Another challenge of computer vision technology is the need for advanced software and hardware solutions. Without an evolution in these components, it is extremely difficult to successfully integrate computer vision into a real-world application.

In terms of the “big picture” of the future of computer vision, all signs point to the promising potential to transform how our world does business.

Unsure of Where to Start with Your Business Needs? Talk to AI Expert Speaker Susan Sly Today.

Are you interested in how to use AI technology to reach your business goals? Susan Sly is an industry expert in artificial intelligence with extensive experience in the fields of machine learning and computer vision.

For more information about the future success of your business can be powered by AI, contact Susan Sly today.

Susan Sly

Author Susan Sly

Susan Sly is the CEO and Founder of Step Into Your Power Inc., the Co-CEO of RadiusAI, keynote speaker, best-selling author, and tech investor. Susan has been featured on CNN, CNBC, Fox, Lifetime, ABC Family, and quoted in Forbes Online, Marketwatch, Yahoo Finance, and more. She is the mother of four and has been working in human potential for over two decades.

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