Explore all aspects of computer vision in relation to image processing through in-depth analysis

What is the difference between image processing and computer vision? Both are concerned with the images. And that’s the only thing they have in common. Computer vision and image processing are two distinct tools with different applications. In this article, we will look at each of them in more detail and explore the differences between them.

Image processing

As the name suggests, the image is processed in image processing. This means that an input file has undergone at least one modification. And with the help of dedicated software, it can be done by one person.

A number of transformations are performed automatically. Sharpening, dithering, smoothing, and edge detection are just a few. They all come completely alone. A chart only needs to start a specific operation. Resizing, stretching, enhancing, and adding new layers or words are all examples of manual transformations. These processes require a greater level of focus and action from the graph. In image processing, you start with the X image, process it, and then get the Y image as a result.

computer vision

It’s a different story when it comes to computer vision. An image or video is used as input to computer vision, but nothing changes in the file itself. The objective is to deduce the meaning of the image and its components. While some image processing methods are used by computer vision to solve problems, processing has never been the primary focus. In fact, image processing algorithms are used to accomplish computer vision tasks.

In this case, computer vision is used to help the driver, especially in bad weather. It examines the environment around the car and assesses potential hazards, obstacles, and other relevant events a driver may encounter while driving, such as a person crossing the street.

Computer Vision vs Image Processing

Computer vision in the automotive industry

As stated earlier, one of the largest industries in which computer vision is used is the automotive industry. Consider the following examples. Did you know that more than 3,000 deaths occur every day in traffic accidents? Computer vision and image processing are just two of the many methods available to solve this problem. Computer vision technologies can potentially be used to solve the problem of distracted driving.

Anyone who has driven a vehicle after a bad night’s sleep can attest to the fact that it is downright dangerous! Therefore, computer vision technology can help you stay awake and determine when you’re too tired or sleepy to drive. Based on your visual condition or head movements, the computer vision program can continuously check your condition. Computer vision and image recognition technology could detect when you’re not paying attention to the road and you’re about to fall asleep. Your vehicle sends you an alarm to get you back on track or suggest you sleep before getting back on the road.

Computer vision in manufacturing

On manufacturing lines, Pharma Packaging Systems uses computer vision technology to efficiently count capsules. In addition, computer vision techniques are used to control manufacturing processes. Additionally, computer vision helps companies in a variety of ways, such as verifying product components against production specifications, analyzing lids, and determining fill levels.

Fitness and sports

Sentio has created a platform to track and analyze football players, giving coaches a complete picture of their matches. In addition, computer vision and image processing systems are used to increase shooting accuracy during sports training (the Noah system), as well as to help swimmers improve their technique by collecting data in real time on everything from hit frequency to speed and execution time (FINIS LaneVision).

Improving the image in the health sector

Image enhancement is a method of improving image quality and perceptibility that is commonly used in modern healthcare. This is used in medical imaging to reduce noise and brighten detail to improve the visual representation of the image. Moreover, this method incorporates both objective and subjective improvements. It turns out that many medical imaging modalities, such as CT, MRI, and X-rays, have limited contrast. As a result, the image quality degrades. This is why image enhancement is so important.

Image processing for missing persons

Image processing technology is used to locate missing persons in Australia. The Missing Persons Action Network (MPAN) uses Facebook to quickly get the word out to friends of a missing person. Thanks to Facebook’s facial recognition techniques, the application can also identify people against the background of photographs. As a result, having a large network of friends increases your chances of meeting new people.

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