The digital image is a representation of the depth of light function in discrete form on a two-dimensional plane.
7. Binary ImageÂ
A binary image is a valid image of pixels that are colored in one of two colors, usually black and white.
8. Grayscale imageÂ
A grayscale image is an image whose pixel depth value is based on a gray gradation.
Examples of the application of computer vision (computer vision) in life:
The company is called ADNOC, which makes about three million (barrels) a little bit of oil and 10.five barrels. billion feet of peeling raw gas every day. Founded in 1971, the Abu Dhabi National Oil Company or ADNOC is a diversified joint force and chemicals company. The Emirati company has previously used a labor-intensive way of classifying representative rock characteristics that required valuable time and energy from a geologist. Once the geologists enter the high-determination rock images into the database, they use IBM Watson to properly describe and log carbonate rock classes, giving ADNOC the ability to categorize up to 25,000 thin section rock images per day. With Watson, they can control a single set of drawings for an entire water tower in minutes, saving their geologists valuable time.
The second case is about how computer vision is changing the hiring process. In the HR world, computer vision is changing the way candidates are hired in the interview process. Knockri, a Canadian-based startup, creates a stream using their senses to consider their AI video skills. Using computer vision, machine learning, and data science, they can measure skills and run initial candidate assessments to help large companies select candidates. Another case of how computer vision is revolutionizing processes is that it can create other specific classifiers to determine rust levels based on certain structural rules or standards. So for example, you could have grade 1 rust, which cools down to very minimal rust to grade 6 rust, which will be more severe. And finally, by sketching through the second categorization to find the rust, it is possible to determine the rust level for a particular area of the metal-containing image. Using metal groupings and rust classifiers when scaled down on thousands or millions of images can save insurance offices millions of dollars.
