For decades, mankind has dreamed of creating machines with the attributes of human intelligence, those that can think and act like humans. One of the most enthralling ideas was to give computers the power to “see” and interpret the world around them. Let us explore the fiction of yesterday that has become the fact of today – COMPUTER VISION!

What is Computer Vision?

An interdisciplinary scientific field that pacts with how computers can gain advanced understanding from digital images or videos are how Computer vision can be perfectly defined.

Computer vision

Tasks of Computer vision include methods for acquiring, processing, analyzing, understanding digital images, and extracting high-dimensional data from the real world to produce numerical or symbolic information. The scientific aspect of computer vision is apprehensive with the theory behind artificial systems that extract information from images. The image data can be molded into many forms such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning device. Whereas the technological aspect of computer vision applies its theories and models to the construction of computer vision systems.

How does computer vision work?

Computer vision technology tends to imitate the way the human brain functions. A popular hypothesis states that our brains rely on patterns to decode individual objects and thus helping in recognizing visual objects. This concept is further used to create computer vision systems.

Computer vision algorithms that are used today are based on pattern recognition. Computers are trained on massive amounts of visual data, further the computers process images, label objects on them, and find patterns in those objects. As a result, the computer is equipped enough to accurately detect what a particular image is every time a similar image is used.

Top 10 real-world applications of computer vision

Facial Recognition

Face Recognition algorithms seize more than just features. Facial recognition in computer vision is programmed in a manner that empowers it to capture all the unique factors of a face and from multiple angles. As a result, the computer can measure and memorize the gap between an individual’s eyes and mouth. Siamese Network in computer vision is generally used to carry out facial recognition.

Computer vision Facial recognition

Augmented Reality

Augmented Reality (AR) first conquered a real-world environment and then added the computer-generated input to it. Several parts from both of the environments, real and augmented, can interact together and even digitally influence. Therefore, AR can simply be defined as the best combination of both worlds. Augmented Reality in computer vision renders a 3D registration of real and virtual objects.

Social Distancing and UAVs

Amidst the pandemic, every single life is at the risk of getting infected from the virus and social distancing is very well the need of the hour. Via computer vision cameras and sensors, we can effectively monitor public spaces to track and ensure social distancing and impose strict rules and punishments for those who violate these norms. Computer Vision-enabled drones or UAVs are also serving a variety of purposes like delivering emergency food supplies and testing kits, spraying disinfectant for sanitization of public places, detecting unmasked citizens via cameras, conveying advisory through speakers, and analyzing the movement of people in quarantine shelters.

Computer Vision Drones

Machine Vision

Machine Vision can be defined as a set of techniques to enable image-based automation for business actions like process control, automated inspection, robot guidance, etc. It is a branching of systems engineering that unified existing technologies in newer ways and use them to solve real-world problems.

Self-driving cars

Computer vision has helped to bring the vision of self-driven cars to reality and this the new hype in the automobile industry presently. The gen-z acronym, YOLO (You Look Only Once), is a very popular computer vision algorithm used for autonomous driving which can efficiently detect objects in the path. Computer vision allows cars to make sense of their surroundings. Cameras that capture videos from different angles and send videos as an input signal to the computer vision software helps to meet the demands of autonomous driving. The system further processes the video in real-time and detects objects like road marking, objects near the car, traffic lights, etc. Tesla’s autopilot mode is one of the most notable examples of computer vision applications in the automobile industry.

Computer vision self-driven cars

Optical Character Recognition

OCR is the electronic conversion of images comprising of handwritten text into machine-encoded text. It further includes text processing in different forms, such as a photo of a document, a scanned document, subtitles superimposed on an image, or a scene photo. Many computer vision algorithms are used for OCR technology, like matrix matching.

Visual Search

Visual search uses images as keywords like texts and looks out for related images, websites, blogs, or any other posts. Visual Search Engine is programmed in a manner that channels the time gap between the search made and findings.

Gesture Recognition

It is of no surprise that to settle in with human convenience, multiple algorithms exist in the computer vision field to detect human gestures and postures. These algorithms can interpret human gestures initiated from any motion or state of the human body.

Healthcare

Computer Vision also sets a wide range of applications in the healthcare sector. It can effectively assist medical professionals in training allowing the doctors to interpret medical images used in techniques like X-Ray and MRI. Image information is an important element for diagnosis in medicine as it accounts for 90 percent of all medical data.

Computer vision and healthcare

Agriculture

Several agricultural organizations utilize computer vision to keep track of the harvest and solve the common agricultural crisis such as weeds emergence or nutrient deficiency. Computer vision systems handle images from satellites, drones, or planes, and attempts to detect the problems in the early phase, which further helps the farmer to avoid unnecessary financial losses.

Conclusion

Computer vision is a popular topic in articles about the latest technology. This technology also validates an important step that civilization makes toward creating artificial intelligence that will be as refined as humans.