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Difference between Computer Vision and Image Processing
The human eye has six and seven million cone cells, each of which contains one of three different types of opsins, which are proteins that are sensitive to color. When photons of light strike these opsins, they transform, setting off a cascade that produces electrical signals, which then send the messages to the brain so that they can be interpreted.
Making a machine that can read this on a level that is understandable to humans has always been a difficult task because the process as a whole is so complicated. Emulating human vision in terms of recognizing patterns and faces, as well as translating 2D pictures from a 3D world into 3D, is at the heart of the modern-day machine vision system, which was designed with the motivation to mimic human vision.
On a conceptual level, image processing and computer vision share a lot of similarities, and the vocabulary for these two fields-which is sometimes confused with one another-is frequently employed interchangeably. In this section, we will provide a high-level review of the methods, as well as an explanation of the main differences between them.
What is Image Processing?
As the phrase "image processing" suggests, this technique involves "processing" an image. It denotes that an input file has been subjected to at least one alteration. Additionally, a person with the help of some specialized software can accomplish this task (to name just Photoshop, InDesign, GIMP, Gravit, CorelDRAW and many more).
Some of the modifications are carried out by themselves automatically. Such modifications include contrast enhancement, edge detection, sharpening, and filtering. There is no human involvement at any point in any of these processes. It is sufficient for a graphic to initiate a specific activity. Resizing, stretching, improving, and adding new layers or texts are examples of the types of transformations that fall under the category of manual work. These procedures necessitate a great deal more focus and activity on the part of the graphic. In image processing, you begin with an image X, process it, and then you end up with an image Y as a result of your work. The setting, the goal, and the problem that needs to be solved all play a role in determining what kinds of changes are required.
What is Computer Vision?
When we talk about computer vision, though, things take on a very different tone. In computer vision, an image or video is used as input, but the file itself is not modified in any way during this process. The objective is to make sense of the picture and what it depicts. Although some of the image processing algorithms might be utilized in the process of computer vision problem solving, the processing of images is never the major focus. In reality, the procedures of image processing are utilized in order to accomplish the responsibilities of computer vision.
The automotive industry is home to one of the most significant applications of computer vision today. In this scenario, computer vision serves as an assistant for the driver, which is especially helpful when the weather is terrible. It examines the area around the vehicle and looks for potential dangers, impediments, and other important circumstances that require a reaction from the driver while they are driving. One example of such a situation is a person crossing the street.
Applications of Computer Vision and Image Processing
In this section, let's throw some light on how the concepts of Computer Vision and Image Processing are put into practice across a variety of industries
Computer Vision in the Automotive Sector
The automotive sector is one of the most prominent areas where computer vision is finding its applications. Consider some examples. Did you realise that there are over 3,000 persons who lose their lives in car accidents every single day? Way too much, and one of the numerous approaches to addressing this issue is to make use of computer vision and image processing. The computer vision technology also has the potential to be utilised to combat the problem of distracted driving.
The National Highway Traffic Safety Administration (NHTSA), which is part of the United States Department of Transportation, believes that driver distractions are the cause of more than 3,000 deaths that are related to automotive accidents. Everyone who has ever gotten behind the wheel of a vehicle after a restless night will attest to the fact that it is a highly risky thing to do. Computer Vision can assist you in remaining awake and in determining whether or not you are too exhausted to safely operate a vehicle. The programme that uses Computer Vision can continuously check your condition by analysing the status of your eyes or the movements of your head.
Computer Vision in the Production Industry
Computer vision is utilised by Pharma Packaging Systems in order to carry out automated counting of tablets and capsules on production lines. In addition to that, computer vision methods are also utilised in order to regulate the assembly processes. In addition, businesses can use computer vision to perform tasks like as analysing lids and fill levels, checking product components to ensure they meet production criteria, and more.
Computer Vision in Athletics and Fitness
Sentio has developed a program that can track and analyse football players, giving football coaches a more complete picture of how games are played. 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.
Image Processing in Medical Imaging
Image enhancement is a technique that is utilised extensively in contemporary medical care to increase the quality of images as well as their readability. The visual representation of an image can be improved by using this technique to reduce noise and sharpen details. This technique is used in medical imaging.
In addition, this method incorporates both objective and subjective enhancements into the process. It turns out that a lot of the medical imaging techniques, such X-rays, CT scans, and MRIs, have a problem with having too little contrast. Because of this, the image's overall quality will suffer. This is the primary reason why image editing is essential.
Image Processing in the Search for the Missing
The use of image processing technologies is essential in the search for missing persons. Facebook is utilised by the Missing Persons Action Network (MPAN) to facilitate the rapid dissemination of information among the friends of a person who has gone missing.
In addition, the programme can recognise persons within photographs despite the presence of backgrounds by utilising Facebook's algorithms for facial recognition. As a direct consequence of this, the possibilities of finding people through a vast network of friends unquestionably expand.
Comparison between Image Processing and Computer Vision
The following table highlights the major differences between Image Processing and Computer Vision −
| Basis of Comparison | Image Processing | Computer Vision |
|---|---|---|
| Definition | Processing the raw images that are entered into the system in order to improve them or get them ready for usage in other applications is the primary emphasis of image processing. | The primary objective of computer vision is to derive information from the pictures or videos that are used as input in order to have an accurate grasp of the data and to anticipate the visual data in the same way that the human brain does. |
| Applicable methods | Methods such as anisotropic diffusion, hidden Markov models, independent component analysis, different filtering, and many more are utilised throughout the image processing process. | Image processing is just one of the many techniques that are employed in computer vision; other approaches, such as machine learning, CNN, and so on are also utilised. |
| Function | The field of Computer Vision includes Image Processing as one of its subfields. | Image processing is the subfield that Computer Vision falls under. |
| Applications | Some applications of image processing include rescaling the image (also known as digital zoom), correcting the illumination, and changing the tones, among other things. | Object detection, face detection, handwriting recognition, and other similar tasks are all examples of applications that computer vision can perform. |
Conclusion
The methods that are used in Image Processing can alter images in a variety of ways, including sharpening, smoothing, filtering, enhancing, restoring, and blurring amongst others.
Computer vision, on the other hand, is concerned with deciphering the meaning of what may be seen by computers. A computer vision system receives an image as an input and generates images as an output based on a particular job, such as the labeling of objects and their coordinates.
Both of them collaborate in many different situations; in fact, many computer vision systems depend on image processing techniques to function properly. Image processing entails the processing of raw input images, as well as the enhancement of those images or the preparation of those images to carry out certain activities.
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