Computer Sciences and Information Technology
Subject: Computer Vision in Artificial intelligence
Topic: facial recognition
Question to be answered: How does computer vision effect our day to day life?
Topic:
Introduction to Intelligent Systems / Artificial Intelligence
Facial Recognition
Technological advancements over the years have improved computer vision which is the ability of computers and machines to recognize and understand sight. The definition of computer vision is the area of computer technology that involves the methods of processing, analyzing and comprehending images and high-dimensional data from the real environment to produce decisions in the form of numerical and symbolic information. Computers and machines can process and understand videos and images to extract meaningful information and actionable decisions.
Computer vision has affected the health care sector by providing essential services of medical imaging in vital components of health care applications. The healthcare sector uses computer vision in multimodal image fusion, image segmentation, image-guided therapy, and computer-aided diagnosis. This technology has helped doctors and patients in increasing the level of health care delivery. The use of computer vision has improved the diagnosis of illness as the technology provides an accurate classification of conditions and illness that reduces or eliminates incidences of incorrect treatment and incorrect diagnosis. For instance, doctors are can use Gauss surgical which is a computer vision technology that monitors blood loss during medical situations.
Cainthus is an animal facial recognition technology based on computer vision. This technology was developed for agricultural purposes and is useful in predictive imaging analysis that performs the main function of monitoring the health condition of crops and livestock. The technology-based computer vision identifies individual cows within a short period by connecting and relating hidden patterns and facial recognition. It keeps track of the food and water intake, detects heat, and the behavioral patterns of animals and crops. The information processed by an AI-powered algorithm that generates alerts to the farmers to make decisions immediately on reproduction, milk production, and the overall health of the animal. The technology of Cainthus also monitors the features of crops and analysis the rates of growth, the general health of the crop and fruit ripeness, and also the crop maturity.
The technology of computer and machine vision is applied in image recognition applications. The computers and machines classify the data they extract from documents and authenticate the documents. Documents which can be recognized are ID cards, Driver’s license, checks, and passports. The technology is much easier in the verification of authentic documents. The customers are required to take a picture of the ID or any document through the use of the mobile phone. The picture is processed and analyzed by computer vision technology for authenticity. Following confirmation that it is authentic; the application is processed. Mitek Systems, an image recognition application works through this principle of computer vision technology. These systems are gaining popularity in the banking industry to ease the pressures of presenting the checks physically.
Computer vision is also used in manufacturing processes to confirm product quality in the quality control department. Computer vision is more reliable in making quality control decisions than humans. The technology of product quality based on computer vision uses cameras and lighting that captures the images of the products in the manufacturing line. The images are processed by the computer and compared to a predetermined quality standard of the authentic product. The process is highly efficient and reliable than human product quality processing. Computer vision and Artificial intelligence have been integrated to perform dangerous and fatal tasks for humans; thereby reducing the risk to human workers. Such activities include firefighting, mine disposal, mining, and the handling of highly radioactive materials.
In the automobile industry, computer vision has been used to develop self-driving cars with the determination to reduces and eliminate incidences of accidents on the roads and anywhere. Companies such as Waymo and Tesla have developed models that make use of computer vision to detect the movements of objects around the car. The car computer vision detects pedestrians, cyclists, other vehicles, and roadwork with the main aim of getting the car to navigate safely through daily traffic. The automobiles version of the computer vision focuses on prediction, planning, and mapping. The automobiles using the technology can give way to emergency vehicles, stop for crossing pedestrians, and allows space for parking cars.
In conclusion, computer vision is currently being utilized in all industries daily. Technology is needed both at home and in the workplace. More advancements in technology will increase the use of computer vision in our daily lives. Computer vision applications have emerged in Agriculture, healthcare, automobiles, and industrial sectors that have embraced this technology to increase effectiveness and reduce incidences of human error. All industries and all areas of our lives should take up to this technology to eliminate human error and most important to attain accuracy of the processes involved. Computers and machines are effective in their functioning and rarely make incorrect decisions and actions on any predefined issue. However, the existing computer vision technology is still reliant on humans to function in monitoring, interpreting, controlling, making decisions, and taking actions. Generally, computer vision is very useful in our daily lives and its only helpful to integrate more of this technology into our daily lives.
References
Das, A., Degeling, M., Wang, X., Wang, J., Sadeh, N. and Satyanarayanan, M., 2017, July. Helping users in a world full of cameras: A privacy-aware infrastructure for computer vision applications. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 1387-1396). IEEE.
Rewari, S., Shaha, A. and Gunasekharan, S., 2016. Facial Recognition Based Attendance System. Journal of Image Processing & Pattern Recognition Progress, 3(2), pp.43-49.
Takita, K., Muquit, M.A., Aoki, T. and Higuchi, T., 2004. A sub-pixel correspondence search technique for computer vision applications. IEICE transactions on fundamentals of electronics, communications and computer sciences, 87(8), pp.1913-1923.
Venkatraman, S., Balasubramanian, S. and Gera, D., 2017, December. Multiple face-component analysis: A unified approach towards facial recognition tasks. In 2017 2nd International Conference on Man and Machine Interfacing (MAMI) (pp. 1-6). IEEE.
Middi, V.S.R., Thomas, K.J. and Harris, T.A., 2018, December. Facial Keypoint Detection Using Deep Learning and Computer Vision. In International Conference on Intelligent Systems Design and Applications (pp. 493-502). Springer, Cham.
