Computer vision (CV) and image processing are closely related, which use artificial intelligence (AI) and pattern recognition techniques to extract meaningful information from images, videos, and other visual inputs.
The image processing industry is leading the way in a technological revolution called 4.0, which is laying the foundation for new technologies in image data processing and object recognition. Image processing algorithms are used to extract information from images, retrieve and compress image and video data, and create new experiences in virtual and augmented reality.
What is Computer Vision?
Computer vision is a branch of computer science that focuses on enabling computers to recognize people and objects in images and videos.
Computer vision combines cameras, edge or cloud computing, software, and artificial intelligence (AI) to enable systems to “see” objects and identify them.
Computer vision works like human vision, except humans have a better start. Human vision has the advantage of having the full context of life, allowing the eye to distinguish objects from one another, to determine the distance between them, to know whether they are moving, and if any If there is a problem, the image can train it.
Computer vision trains machines to perform these tasks, but they have to do it in much less time, using cameras, data, and algorithms instead of retinas, optic nerves, and the visual cortex. Because systems trained to inspect products or monitor production assets can analyze thousands of products or processes per minute, noting defects or problems that humans cannot identify, it surpasses human capabilities.
Working of Computer Vision
Computer vision relies primarily on pattern recognition techniques for self-training and understanding visual data. The availability of vast data and companies’ willingness to share it has made it possible for experts in deep learning to use this data to make the process more accurate and faster.
Although machine learning algorithms were previously used for computer vision applications, deep learning methods have developed as a better solution for this field. For example, machine learning techniques require a huge amount of data and active human monitoring in the initial monitoring phase to ensure the results are most accurate. Deep learning relies on neural networks, uses examples to solve problems, and learns self-learning by using classified data to recognize common patterns in examples.
What is Image Processing?
Image processing is a method of performing certain operations in an order to obtain an improved image or extract some useful information from it. It is a type of signal processing where the input is an image and the output may be an image or properties/features associated with that image. Nowadays, image processing is among the fast-emerging technologies. It is a major area of research in engineering and computer science as well.
Image processing is an analytical step that analyzes and digitally processes an image before we can feed a model with input.
Image processing is a way to convert an image into a digital form and perform some mathematical operations on it, to get an improved image or extract other useful information from it.
Image processing mainly includes the following three steps:
- Import the image using image acquisition tools.
- Image analysis and manipulation.
- The output can be a modified image or a report based on image analysis.
Working of image processing
Applying image processing methods requires first converting the image or photograph into a digital image file. Then numerical methods can be applied to rearrange parts of the image, improve color separation, or to improve shadow quality.
For example, image quality can be improved by using image processing techniques. These techniques are widely used in commercial art applications that involve enhancing and rearranging portions of photographs and other artwork. Similar methods are used to analyze satellite images of the Earth and images of galaxies.