As we see today, the internet world is flooded with various kinds of images. Smartphones, cameras, and taking photos or videos and sharing them is not a difficult thing, which has resulted in the tremendous growth of modern social networks like Instagram. Also, YouTube is probably the second largest search engine with hundreds of hours of video uploaded every minute and billions of videos watched every day. Indeed, the internet consists of text and images. Indexing and searching for text is relatively easy, but to index and search for images, an algorithm (computer vision algorithms) needs to know the contents of the image.
For a long time, the content of images and videos was still blurry, and that was explained using the meta description provided by the person who uploaded them. So, to get the most out of image data, we need a computer to “see” in the sense of having a vision of the image and understanding its contents. In this regard, on this occasion, we will discuss in more detail the definition of computer vision (computer vision), types and examples, and their importance in the future.
This means computer vision is a field of computer science that focuses on creating digital systems that can process, analyze, and understand visual data (images or videos) in the same way that humans do. As the IBM Site also explains, this computer vision obtains meaningful information from images, video, and other digital visual input systems (read the definition of the digital system here) and takes action or makes recommendations based on that information.
The concept of computer vision is based on teaching computers to process images at the pixel level and understand them. Technically, the engine attempts to take visual information, handle it, and interpret the results through special software algorithms. It is why this technology has been developed and used everywhere in any sector.