Difference between Image Processing and Computer Vision

These terms are often related so many people confuse them or use them interchangeably. However, there is a clear difference between image processing and computer vision. Therefore, in this article, we decided to look at both disciplines to understand the nuances within the two concepts.

With the development of the Internet and modern technologies, we can now confirm that we live in a society of images. Nowadays, anyone can use their smartphone’s camera to take a photo or record a video and share it online or on social media. In addition, in every minute, hundreds of hours of videos are uploaded by Internet users to platforms such as YouTube or Vimeo. All these images that end up on the Internet are full of all kinds of data that are invaluable to companies. However, computers need not just “see” the image. Devices must understand its content to manage and interpret data. This is the principal objective and difference between image processing and computer vision.

What is image processing?

Image processing is a discipline of computer science and applied mathematics that studies images and their transformations. Image processing involves using complex algorithms that enable manipulating the image. By applying several transforms to the input image, image processing solutions create and return the final result image. The fundamental modifications include sharpening, smoothing, and stretching. The change applied depends on the context and the problem to be solved.

As you can see, image processing is a subset of computer vision, and its use is limited to processing raw input images to correct them or prepare for other, further tasks. Image processing applications include lighting correction, tonal modification, and image resizing. The most recognizable include:

  • Normalizing the image’s photometric properties
  • Cropping its edges
  • Elimination of noises

What is computer vision? 

Computer vision is the field of artificial intelligence and machine learning that studies technologies and tools that train computers to perceive and interpret visual information from the real world. The overarching task of this technology is to extract data from input materials, i.e., images or videos, to understand and predict visual inputs just like the human brain does. However, simply connecting the camera to a computer is not enough. The challenge is to classify and interpret objects in pictures and videos, their relationships, and the context. We want computers to explain what’s in an image, video clip, or video stream in real-time. For a machine to recognize visual objects, it must be trained on hundreds of thousands of examples.

In computer vision, all devices are designed in such a way as to be able to obtain the highest possible level of understanding of the input digital images or movies with their help. This makes it possible to automate many of the tasks usually performed by the human visual system. For this purpose, it uses many advanced techniques, and image processing is just one of them. Therefore, computer vision is for image processing a superset.

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Image processing and computer vision – the primary differences

Since both of these fields of computer science deal with working with images and video, the difference between the two may not be fully understood or noticeable at first glance. Understanding the subtle discrepancies between image processing and computer vision requires a deeper analysis. Contrary to appearances, the main difference between the two approaches is the goals and not the methods used. For example, image processing can be used if your goal is to enhance an image for later use. On the other hand, computer vision is for such tasks if the goal is to recognize objects or automatically drive a vehicle.

Precisely, image processing is used for the following purposes.

  • Image visualization: is the presentation of ready and processed data in visualization so that the recipient can understand it better
  • Image enhancement: by sharpening and restoring the image
  • Image search: this involves retrieving the image source from an investigation by the Image Search engine
  • Perform a classification: its purpose is to extract individual objects and locate their position in the image

In turn, computer vision is usually used to identify and recognize images. Image recognition is currently used in many applications such as medical imaging, security surveillance, facial recognition, logo, and building identification. These are just some examples of the wide range of computer vision applications. Computer vision applications also have numerous successes, especially in robotics, text detection, face recognition, e-commerce, and image classification. However, as mentioned earlier, the images must first be annotated, segmented, or otherwise processed for these models to work.

Image Processing and Computer Vision need to cooperate for the future

In many cases, image processing and computer vision are used parallel and cooperate. Indeed, a significant proportion of many of today’s computer vision systems relies on image processing algorithms. Images are transformed by processing algorithms by many methods, mainly by blurring, sharpening, or applying filters. Thanks to these corrections, computer vision can focus on the correct interpretation and understanding of what the machines see.

Combining both technologies makes it possible to generate great visual experiences and create value in many areas of science and for life-saving solutions in medicine. Furthermore, since both image processing and computer vision are two areas that are constantly being developed, we can admire the creation of newer and more advanced programs and applications with little excitement.

Image Processing and Computer Vision for business

The potential of image processing and computer vision convinces more and more companies worldwide to implement these modern solutions in their enterprises to improve the standard of products and services offered. However, implementing modern technologies based on artificial intelligence or machine learning is not simple. Therefore, for this purpose, they often decide to cooperate with a trusted partner experienced in computer vision consulting.

The success of a venture depends on the knowledge of technology, appropriate know-how, the ability to identify the organization’s needs and find the right solutions. There is no doubt that image processing and computer vision allow companies to obtain precise results and lower operating costs.

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