Computer vision is a branch of technology that allows a computer to observe objects in its environment. If people are taught to recognize an object for a cause, computer vision may be taught to do so as well. For example, if we view a capture of a car, we will immediately recognize her just by looking at it. This occurs because we already know the car well enough that our brain can comprehend all of the information with just a little input.
Computer vision is an informatics engineering field that allows a computer to "see" items in its environment. The aim of "seeing" this is so that the computer can analyze the image in front of it and convert the data into commands. Computer vision enables machines to recognize and analyze items in the same way that people do. The most common example we utilize now is the use of scanQR on smartphones to make payments. How does a QR Scan, for example, work? When a computer (in this example a smartphone) detects the scanQR code, it sends a particular command, which might be a payment order, unlocking, or other actions.
Computer vision is an area of research that investigates how computers can recognize objects that are observed. This field of research, when combined with Artificial Intelligence (AI), will be able to create a visual intelligence system (Visual Intelligence System). Computer Vision, on the other hand, is concerned with the study of how computers can distinguish objects that are observed / observed. Image Processing and Pattern Recognition are combined in Computer Vision. The field of image processing (image processing) is concerned with the alteration of images (image). The goal of this procedure is to improve image quality.
Image Analysis, Image Processing, and image interpretation is the key towards computer vision. The details for each process are shown below.
Computer vision offers numerous advantages that will be extremely beneficial to humanity. The following is an example of how computer vision can be used in different fields.
Farmers can now efficiently cultivate ever-larger areas thanks to modern technology. At the same time, this means that these areas must be inspected for pests and plant diseases, as plant diseases can result in costly harvest losses and crop failures if not addressed.
Because drones, satellite photos, and remote sensors can generate vast volumes of data, machine learning can help. The collecting of numerous measurable values, parameters, and statistics, which can be monitored automatically, is made easier by modern technology. Despite the massive planting of larger fields, farmers have a 24-hour view of soil conditions, irrigation levels, plant health, and local temperatures. Machine learning algorithms analyze this data so that the farmer may utilize it to react quickly to possible problem areas and efficiently allocate available resources.
Agriculture is particularly interested in computer vision since picture analysis enables for early detection of plant diseases. Plant diseases were often only discovered after they had already spread a few years ago. Early warning systems based on computer vision can now detect and stop the widespread spread at an early stage. Because proportionally smaller areas need to be treated, producers lose less crop and save money on countermeasures like pesticides.
The automated identification of wheat rot, in particular, has gotten a lot of attention from the computer vision field. Various strains of this aggressive fungus infect cereals in East Africa, the Mediterranean region, and Central Europe, resulting in significant wheat crop losses. Because the pest is easily apparent on cereal stems and leaves, trained image recognition algorithms can detect it early and prevent it from spreading.
Self-driving cars are undoubtedly among the artificial intelligence use cases that have garnered the most media interest in recent years. This is most likely due to the idea of autonomous driving being more futuristic than the technology's actual effects. It contains a number of machine learning challenges, but computer vision is a key part of their solution.
The algorithm (or "agent") that controls the car, for example, must be aware of the car's surroundings at all times. To adapt to the changing environment, the agent needs to know how the road works, where other vehicles are in the area, the distance to potential barriers and items, and how fast these objects are moving on the road. Autonomous vehicles are outfitted with large cameras that film their surroundings across a large area for this purpose. An image recognition algorithm monitors the generated material in real time. This, like Customer Behavior Tracking, necessitates the algorithm's ability to find and identify relevant things not only in static photos but also in a continuous stream of images.
This technology is already in use in the industrial sector. The challenge in training an algorithm to eliminate even possible failure of the agent in complex exceptional conditions arises from the complexity, volatility, and difficulty of road traffic.
For years, the sport industry has been undergoing digital upheaval. New training methods and trends are broadcast to millions of people on YouTube, training progress is tracked and assessed using applications, and virtual training and home workouts have exploded in popularity since the onset of the corona crisis. Fitness trainers are essential for studio support, especially in weight training, due to the significant risk of injury – until recently. While checking one's own posture and position during training via video is already widespread practice, computer vision allows for a more accurate evaluation and assessment of video content in this sector than the human eye.
Attention Tracking, a technique that has already been deployed in the retail business, is used. An algorithm can recognize and estimate the posture and stance of individuals on video using Human Pose Estimation. The position of the joints and their relationship to one another is determined for this purpose. Deviations from the optimal and safe execution of a fitness routine can be spotted and highlighted automatically because the algorithm has learned what that looks like. This can be done in real-time and with an immediate warning signal on a smartphone app, rather than evaluating movements subsequently and warning in time of critical errors. This should lower the risk of injury when strength training, making training without a fitness trainer safer, and lower the expense of safe strength training.
Human Pose Estimation is a step forward in the direction of digital fitness training. Smartphones are already widely used in fitness, and apps that make exercise safer are likely to be highly embraced by a wide audience.
Amazon has always been able to take advantage of the analytical capabilities of their digital platform. The user experience may be improved by analyzing customer behavior in depth. The retail business is also attempting to improve and maximize the customer experience. Until today, there were no methods for automatically recording people's interactions with displayed things. For the retail industry, computer vision is now able to bridge this gap.
Algorithms can automatically analyse video material and examine client behavior when used in conjunction with current security cameras. The current number of people in the store, for example, can be counted at any time, which is a valuable application during the COVID-19 pandemic, which imposes limits on the maximum number of visitors allowed in stores. However, individual-level analysis, such as the preferred route through the store and individual departments, may be more intriguing. This enables for better product design, structure, and positioning, as well as the avoidance of traffic congestion in frequently visited departments and an overall better user experience for customers. The ability to track how much attention certain shelves and products receive from customers is revolutionary. Passers-by can use specialized algorithms to determine the direction of people's gaze and hence measure how long they look at any particular thing.
With the use of this technology, retailers can now catch up with online commerce and analyze client behavior in their stores in great detail. This boosts sales, cuts down on time spent in the store, and optimizes customer dispersion inside the business.
Computer vision has enormous potential in healthcare, with numerous applications. The examination of pictures, scans, and photographs is crucial in medical diagnosis. Computer vision technologies promise to not only simplify but also to prevent mistaken diagnoses and lower treatment costs by analyzing ultrasound images, MRIs, and CT scans, which are all part of modern medicine's regular repertoire. Computer vision isn't meant to take the position of medical experts; rather, it's meant to make their jobs easier and help them make better decisions. Image segmentation aids diagnosis by detecting key areas on 2D or 3D scans and colorizing them to make black-and-white images easier to examine.
The COVID-19 pandemic is the most recent application of this technology. Physicians and scientists can use image segmentation to discover COVID-19 and assess and quantify the infection and disease's progression. On CT scans of the lungs, the trained image recognition system detects questionable spots. It assesses their size and volume in order to track the sickness of affected people.
The advantages of tracking a novel disease are enormous. Not only does computer vision make it easier for doctors to diagnose and monitor a patient's condition throughout treatment, but it also creates useful data for researchers investigating the disease and its progression. Researchers gain from the collected data and created photos as well, as it allows them to devote more time to experiments and tests rather than data collecting.
Adapted from: Statwork
