Computer Vision vs. Image Processing: What’s the difference?
Exploring every end of computer vision vs image processing through an in-depth analysis
What is the difference between image processing and computer vision? Both are concerned with 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 post, we’ll look at each of these in greater detail and explore the differences between them.
As the name implies, image is handled in image processing. It signifies that an input file has undergone at least one change. And with the help of dedicated software, it can be done by a person.
A number of transformations are carried out automatically. Sharpening, juxtaposing, smoothing, and edge detection are just a few to name. They all happen completely on their own. A graphic only needs to begin a specific operation. Resizing, stretching, improving, and adding new layers or words are all examples of manual transformations. These processes necessitate a greater level of focus and action on the part of the graphic. In image processing, you begin with image X, process it, and then get image Y as a result.
It’s a different story when it comes to computer vision. A picture or video is used as input in computer vision, but nothing changes to the file itself. The objective is to deduce meaning from 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 jobs.
In this case, computer vision is employed to aid the driver, especially, in bad weather. It examines the environment around the car and assesses potential hazards, impediments, and other pertinent events, that a driver may encounter while driving such as a person crossing the street.
Computer Vision vs. Image Processing
Computer vision in the motor industry
As previously said, one of the most important industries in which computer vision is used in the automotive industry. Consider the following examples. Did you realise that over 3,000 deaths occur in traffic accidents every day? Computer vision and image processing are just two of many methods available to address this issue. Computer vision technologies can potentially be utilised to address the problem of distracted driving.
Anyone who has driven a vehicle after a lousy night’s sleep can attest to the fact that it is quite unsafe! As a result, computer vision technology can assist you in staying awake and determining when you are too tired or sleepy to drive. Depending on your visual state or head motions, the computer vision programme can continuously check your condition. Computer vision and image recognition technology could detect when you’re not paying attention to the road and are about to fall asleep. Your vehicle sends you an alarm to get you back on track or to suggest that you sleep before driving again.
Computer Vision in manufacturing
On manufacturing lines, Pharma Packaging Systems uses computer vision technology to count capsules efficiently. Furthermore, computer vision techniques are employed to control manufacturing processes. In addition, computer vision aids businesses in a variety of ways, such as checking product components against production specifications, analysing lids, and determining fill levels.
Fitness and sports
Sentio has created a platform for following and analysing football players, giving coaches a complete picture of their matches. Additionally, computer vision and image processing systems are utilised to increase shooting precision during sports training (the Noah system), as well as to help swimmers enhance their technique by collecting data in real-time on everything from stroking frequency to speed and turnaround time (FINIS LaneVision).
Image enhancement in the healthcare sector
Image enhancement is a method for improving image quality and perceptibility that is commonly employed in modern healthcare. This is used in medical imaging to reduce noise and brighten details in order to improve the image’s visual representation. Furthermore, this method incorporates both objective and subjective improvements. Many medical imaging modalities, such as CT, MRI, and X-ray, have limited contrast, as it turns out. As a result, the image quality degrades. This is why image enhancement is so important.
Image processing for missing people
Image processing technology is utilised to locate missing people in Australia. The Missing Persons Action Network (MPAN) uses Facebook to quickly get the word out to the friends of a missing person. Using Facebook’s face recognition techniques, the application can also identify persons 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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