Computer Vision

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Computer Vision

In this research brief, we started our research by looking for a pre-compiled list of computer vision applications. Having found one, we looked at each data point and made sure that each fully answered our research criteria. Below is an overview of our findings.


the initial search conducted yielded data published by different database on computer vision applications.
After thoroughly checking through all the data provided, we selected our data points based on the research criteria requested for. We choose only the application that parts of its data is from public information.


One of the most important uses of computer vision is in the health sector. Computer vision is already been used to identify and diagnose conditions and illnesses. Arguments are already been made on which one is better, computer vision or sensors. However, it has been proven already that the two systems can be used together to make serious lifesaving medical interventions. An example of this usage is the use of the Gauss Surgical developed a solution that monitors blood loss in real time. The sensors detect the amount of blood located on surgical sponges which are then processed by machine learning algorithms which make a determination how much blood was lost. This technology is been used currently in surfing surgical operations and Caesarian deliveries. This technology can save around $10 billion in unnecessary blood transfusions every year.
Another application of computer vision in the healthcare sector is its usage in microscopy, X-ray, hagiography, ultrasound, and tomography images to reduce white noise and help doctors pinpoint potential problems. The InnerEye initiative is presently working on image diagnostic tools that will definitely help make decisions.
Computer vision is also helping health workers to analyze health data. One of the main challenges the healthcare system is experiencing is the amount of data that is being produced by patients. Patients rely solely on medical personnel to analyze the tons of data and make the right diagnosis. This can be difficult at times. To solve this problem, Microsoft’s project InnerEye is working on solving parts of that problem by developing a tool that uses AI to analyze three-dimensional radio logical images. The technology potentially can make the process 40 times quicker and suggest the most effective diagnosis for the patient.


Computer vision is helping automobile companies develop self-driving cars. Most people believe that the application of computer vision in automobile production will greatly reduce vehicle crashes, injuries, and fatalities. Most new cars currently implement computer vision for lane keeping and obstacle detection. Fully autonomous driving can only be possible one day if more research is done on computer vision and applied accordingly.
Automobile Manufacturers are already investing in autonomous vehicles. Manufacturers such as Tesla, BMW, Volvo, and Audi use multiple cameras, lidar, radar, and ultrasonic sensors to acquire images from the environment so that their self-driving cars can detect road markings and traffic signage to drive safely. In 2014, Tesla launched its driver-assistance system. The system had with only a few features, such as lane centering and self-parking, however, the company planned to accomplish their self-driving cars sometime in 2018. The level of autonomy ranges from fully autonomous (unmanned) vehicles to manned vehicles which still uses a driver or a pilot but they are supported by computer vision based systems.


The advent of computer vision has improved sports in the area of statistical analysis, player performance and ratings. Computer vision is applied by performance analysts to compile format data in sports. Over the years, computer vision has been introduced in sports to reduced human errors of referees or officials, improve player tracking, semantic analysis, and movement analysis in modern football.
Ball and puck tracking on televised sports have been common for a while now, but computer vision is also helping play and strategy analysis, player performance and ratings.
The world of sports involves fast and accurate motion that is very challenging for competitors to master and difficult for coaches and trainers to analyze, and for audiences to follow. Because of the speed of these sports, officials are also bound to miss events and make errors in judgments. Computer vision has helped to ensure that key events are replayed in slow motion. Also, technologies have been developed to aid officials in their duties and improve viewer experience. Some of these technologies are, the hawk eye in tennis, goal-line technology in football, the Fox Trax in Hockey among others.


Computer vision application is helping manufacturers run more safely, intelligently and effectively in a variety of ways. Computer vision is used to monitor equipment to intervene before a breakdown could occur. This type of maintenance using a computer vision application is called Predictive maintenance. Computer vision also helps to monitor and ensure that product quality is maintained at all times. Defective products are also reduced with computer vision.
Computer vision applications are also applied in the quality control departments of A manufacturing process where final products are automatically inspected in order to find defects. Another
an example is the measurement of position and orientation of details to be picked up by a robot arm.

Computer vision application IN Military OPERATIONS

Computer vision applications in Military operations are probably one of the largest areas for computer vision. Examples are the detection of enemy soldiers or vehicles and missile guidance. Computer vision has been applied in the production of image sensors, which provides valuable data of a combat scene.
Missiles defense systems, missile guidance all use computer visions in its development and creation


the adoption of computer vision is gaining grounds in the banking and financial service providers, some banks have begun to implement it in the on boarding of new customers. In 2016, a Spanish banking group BBVA, launched a new process of signing up for their services. New customers could get a bank account within minutes by uploading a photo of their ID and a selfie. The photos are analyzed using technology from the computer application. Another financial service provider to who has set out to computer vision is an online bank based in Germany called Number26, the bank is also working on similar technology, and is planning to implement it in 2018.
Banks also use image recognition software to prevent fraud by authenticating documents via machine learning. For example, let’s say that a customer wants to deposit a check but does not feel like making a trip to the bank. All they have to do is take a picture of the check with their phone or tablet and the bank’s computer vision software analyzes its authenticity. As soon as the system has verified the check, it is deposited to the customer’s account.


the adoption of computer vision is not the same across the different sectors. However, everything that is available today still relies on humans to supervise, analyze, offer insights and make a decision and take action. Even though certain companies are developing technology that would allow for self-driving cars, the reported fatality rates demonstrate that it still some time away from being commercially available. We are seeing the same in other industries as well where computer science is playing a big role, but the human factor is still necessary as well. As this technology continues to evolve and research learn to fine tune it, soon or later, it will replace humans in pretty much every aspect.

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