What is the difference between computer vision and image processing ? Both these disciplines pertain to images. And that is the only common denominator. Computer vision and image processing are two completely different tools used for various purposes. In this article, we will take a closer look at each one of them and see if machine learning can be any helpful.

Both computer vision and image processing are always about an image. However, here applies a comprehensive definition of an image. It can refer to photographs or videos captured by a camera, radar, ultrasound, or even a voice image recorded via a microphone. In general, we can think of three types of images:
· One-dimensional (a signal of amplitude over time, for instance, mono-channel sound waves)
· Two-dimensional (pictures and photos made up of rows and columns of pixels)
· Three-dimensional (videos)

Both computer vision and image processing can relate to any of these types of images. But what exactly is the difference between computer vision and image processing? Time to find out!

Computer Vision Market

Computer Vision vs. Image Processing

Image Processing

In image processing, an image is, as its name indicates, processed. It means that at least one transformation is applied to an input file. And this can be done by a human with the usage of the dedicated software (to name just Photoshop, InDesign, GIMP, Gravit, CorelDRAW and many more).
Some transformations are done automatically. For example, sharpening, contrasting, filtering, and edge detection. All of them happen 100% automatically. A graphic just has to start a given operation. Other transformations are done manually, and these can be resizing, stretching, enhancing, and adding new layers or texts. These processes require much more attention and activity from the graphic. In image processing, you start with an image X, process it, and, as a result, obtain image Y. What transformations are needed depends on the context, purpose, and issue to be solved?

Computer Vision

When we talk about computer vision, it is a different story. In computer vision, an image or a video is taken as input, and nothing happens to the file itself. The goal is to interpret the image and its contents. Computer vision may indeed use some of the image processing algorithms to solve its tasks, but the processing is never a primary goal. Actually, the image processing methods are harnessed for achieving tasks of computer vision.
One of the most important applications of computer vision is in the motor industry. Computer vision is used here as an assistant for the driver, especially in poor weather conditions. It scans the car’s surroundings and analyzes for potential threats, obstacles, and other relevant situations that a driver needs to react to while driving, to name just a pedestrian crossing the road.

Do computer vision and image processing use machine learning?

The short answer is yes. However, it is much more advanced and developed in computer vision. Consider Google Lens. This is a remarkable example of machine learning in computer vision that anyone with an Android smartphone can check instantly. Google Lens is an app that uses some image processing techniques along with machine learning technologies to give you more information about the object you’re pointing at. Google Lens detects an object, interprets it, and provides you with the results. The only thing you have to do is to point your smartphone’s camera at a specific object and take a picture! Google Assistant will tell you more about the object you’re pointing at. And it goes much further! You can take a picture of a given object and instantly search for it in Google search engine, check its model name and price, or even automatically translate a text written in a foreign language into yours.

And what about image processing? In the last article, we talked about medical computer vision and image analysis. It is a perfect example of machine learning in image processing. The healthcare sector uses image processing, mostly for cancer and other disorder identifications. Two main techniques in use are segmentation and texture analysis. The machine learning algorithm can, for instance, detach the portion of the image that shows cancer cells, enlarge it and enhance the quality. This is a significant improvement in a physician’s work! Similarly, machine learning algorithms scan medical images for marks and distortions and analyze them if any are found.

Computer vision and image processing application in different industries

Computer vision in the motor industry

As we established, the motor industry is one of the leading fields, where computer vision finds application. Consider some examples. Did you know that over 3,000 people die every day in a traffic accident?[1] Far too much, computer vision and image processing are one of many tools to solve this problem. The computer vision technology also can be used to deal with a distracted driving issue.

The U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) estimates that more than 3,000 automobile-related fatalities result from driver distractions. And everyone who has ever driven a car after a bad night’s sleep can confirm that–it’s very dangerous! Therefore, the computer vision technology can help you stay awake and identify when you’re too sleepy or weary to drive. The computer vision application can continuously monitor your condition, based on your eye state or head movements. The computer vision and image recognition technologies could pinpoint the exact moment when you’re not focused on the road and falling asleep. Your car immediately sends you an alert (a signal or a vibration) to get you back on the right course or advise you to take a nap before continuing to drive.

Computer Vision in manufacturing

Pharma Packaging Systems uses computer vision to automatically count tablets and capsules on production lines. Moreover, computer vision techniques are also used to control assembly operations. In addition, computer vision helps companies, for example, to check product components with production specifications, analyze lids and fill levels.

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Computer Vision in fitness and sport

Sentio has developed a tool for tracking and analyzing football players, providing football coaches with a comprehensive picture of matches. In addition, computer vision and image processing systems are also used to improve shooting accuracy during basketball training (Noah system), and to assist swimmers in improving their technique by gathering data from the frequency of strokes to the speed and turn time in real time (FINIS LaneVision).

Image enhancement in healthcare sector

Image enhancement is a technique used widely in modern healthcare to improve image quality and perceptibility. Medical imaging uses this for reducing noise and sharpening details to enhance the visual representation of the image. Moreover, this technique includes both objective and subjective enhancements. As it turns out, many medical imaging methods, such as CT, MRI, or X-ray, suffer from low contrast. That leads to deterioration of image quality. This is why image enhancing is indispensable.

Computer Vision and image processing in healthcare

Read more about Computer Vision in Healthcare

Image processing for missing people

In Australia, image processing technology is used to identify missing people. The Missing Persons Action Network (MPAN) uses Facebook to quickly spread the message among a missing person’s friends. Furthermore, the program can identify people against the background of images by using Facebook’s face recognition algorithms. As a result of this, the chances of finding people definitely increase through a huge network of friends.

The future of computer vision and image processing

Talking about the future, we stay with the healthcare sector.

Image Processing: From 2D images to 3D models

The next step in medical image processing is transforming 2D images into 3D models. In general, 3D imaging is a process where a standard 2D picture is converted into a 3D image by creating the illusion of depth. Future image processing will comprise rendering of colors and textures into the 3D model to make it look more realistic than today. With such expressive 3D images, physicians will be able to examine extremely high-quality 3D models of organs and tissues. This, in turn, will noticeably help them to carry out delicate surgeries and make accurate diagnoses.

Computer Vision: forecast

And what about computer vision? According to a just published RIS News report, 10% of retailers have started a major upgrade of their computer vision solutions and nearly 17% more will deploy the technology in the next 12 to 24 months. [2]
So, we can expect that future computer vision will be used in conjunction with other deep learning technologies and other subsets of artificial intelligence to build more potent and advanced applications. Computer vision will play a significant role in the development of artificial intelligence in general. For instance, currently, there are speculations that sooner or later, humanity will be able to create an AGI–Artificial General Intelligence. In brief–that’s AI that matches human intelligence. Of course, this is a thing of the distant future, but the development of computer vision is one of the indispensable components to make it possible.

Computer vision forecast

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Key takeaways

  • Computer vision and image processing are two completely different tools used for various purposes, but both image processing and computer vision are always about an image.
  • Computer vision and image processing are used in various industries such as: motor industry, manufacturing, healthcare sector, fitness, sport and even for finding missing people.
  • The future of computer vision and image analysis are expected to be in conjunction with other deep learning technologies and other subsets of artificial intelligence.

As always, we encourage you to talk with us about implementing artificial intelligence to your company. If you are wondering if modern technologies such as deep learning, machine learning, business intelligence,computer vision and image processing might be helpful–let us dispel your doubts! Give us a call and let’s talk about your company’s needs!

References

[1] Neuromation. How Computer Vision Can Change the Automotive Industry. Aug 8, 2018. URL: https://medium.com/neuromation-blog/how-computer-vision-can-change-the-automotive-industry-b8ba0f1c08d1. Accessed Dec 11, 2019.

[2] Losspreventionmedia.com. Visualizing a More Profitable Computer Vision Future of Retail. URL: https://losspreventionmedia.com/computer-vision-future-of-retail/. Accessed July 23, 2021

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