Computer Vision Meaning, Examples, Applications – Toolbox tech news

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Computer vision uses artificial intelligence to enable computers to obtain meaningful data from visual inputs.

Computer vision is defined as a solution that leverages artificial intelligence (AI) to allow computers to obtain meaningful data from visual inputs. The insights gained from computer vision are then used to take automated actions. This article details the meaning, examples, and applications of computer vision. 
Computer vision leverages artificial intelligence (AI) to allow computers to obtain meaningful data from visual inputs such as photos and videos. The insights gained from computer vision are then used to take automated actions. Just like AI gives computers the ability to ‘think’, computer vision allows them to ‘see’.
Human Vision vs. Computer Vision
Human Vision vs. Computer Vision
Source: Manning

As humans, we generally spend our lives observing our surroundings using optic nerves, retinas, and the visual cortex. We gain context to differentiate between objects, gauge their distance from us and other objects, calculate their movement speed, and spot mistakes. Similarly, computer vision enables AI-powered machines to train themselves to carry out these very processes. These machines use a combination of cameras, algorithms, and data to do so.
However, unlike humans, computers do not get tired. You can train machines powered by computer vision to analyze thousands of production assets or products in minutes. This allows production plants to automate the detection of defects indiscernible to the human eye.
Computer vision needs a large database to be truly effective. This is because these solutions analyze information repeatedly until they gain every possible insight required for their assigned task. For instance, a computer trained to recognize healthy crops would need to ‘see’ thousands of visual reference inputs of crops, farmland, animals, and other related objects. Only then would it effectively recognize different types of healthy crops, differentiate them from unhealthy crops, gauge farmland quality, detect pests and other animals among the crops, and so on.
Two key technologies drive computer vision: a convolutional neural network and deep learning, a type of machine learning.
Machine learning (ML) leverages algorithm-based models to enable computers to learn context through visual data analysis. Once sufficient data is provided to the model, it will be able to ‘see the big picture’ and differentiate between visual inputs. Instead of being programmed to recognize and differentiate between images, the machine uses AI algorithms to learn autonomously.
Convolutional neural networks help ML models see by fractionating images into pixels. Each pixel is given a label or tag. These labels are then collectively used to carry out convolutions, a mathematical process that combines two functions to produce a third function. Through this process, convolutional neural networks can process visual inputs.
To see images just like a human would, neural networks execute convolutions and examine the accuracy of the output in numerous iterations. Just like humans would discern an object far away, a convolutional neural network begins by identifying rudimentary shapes and hard edges. Once this is done, the model patches the gaps in its data and executes iterations of its output. This goes on until the output accurately ‘predicts’ what is going to happen.
While a convolutional neural network understands single images, a recurrent neural network processes video inputs to enable computers to ‘learn’ how a series of pictures relate to each other.
See More: What Is Artificial Intelligence: History, Types, Applications, Benefits, Challenges, and Future of AI
Listed below are five key examples of computer vision that exhibit the potential of this AI-powered solution to revolutionize entire industries.
In 2015, technology leader Google rolled out its instant translation service that leverages computer vision through smartphone cameras. Neural Machine Translation, a key system that drives instantaneous and accurate computer vision-based translation, was incorporated into Google Translate web results in 2016. 
When the app is opened on internet-enabled devices with cameras, the cameras detect any text in the real world. The app then automatically detects the text and translates it into the language of the user’s choice. For instance, a person can point their camera at a billboard or poster that has text in another language and read what it says in the language of their choice on their smartphone screen.
Apart from Translate, Google also uses computer vision in its Lens service. Both services are capable of instantly translating over 100 languages. Google’s translation services are already benefiting users across Asia, Africa, and Europe, with numerous languages concentrated in relatively small geographic areas.
Over the past few years, more than half of Google’s translation toolkit languages have been made available for offline use. As such, no network connection is required for these neural net-powered translations.
Not to be left behind, technology giant Meta (earlier known as Facebook) is also dabbling in computer vision for various exciting applications. One such use is the conversion of 2D pictures into 3D models.
Launched in 2018, Facebook 3D Photo originally required a smartphone with dual cameras to generate 3D images and create a depth map. While this originally limited the popularity of this feature, the widespread availability of economically priced dual-camera phones has since increased the use of this computer vision-powered feature.
3D Photo turns ordinary two-dimensional photographs into 3D images. Users can rotate, tilt, or scroll on their smartphones to view these pictures from different perspectives. Machine learning is used for the extrapolation of the 3D shape of the objects depicted in the image. Through this process, a realistic-looking 3D effect is applied to the picture.
Advances in computer vision algorithms used by Meta have enabled the 3D Photo feature to be applied to any image. Today, one can use mid-range Android or iOS phones to turn decades-old pictures into 3D, making this feature popular among Facebook users.
Meta is not the only company exploring the application of computer vision in 2D-to-3D image conversion. Google-backed DeepMind and GPU market leader Nvidia are both experimenting with AI systems that allow computers to perceive pictures from varying angles, similar to how humans do.
YOLO, which stands for You Only Look Once, is a pre-trained object detection model that leverages transfer learning. You can use it for numerous applications, including enforcing social distancing guidelines.
As a computer vision solution, the YOLO algorithm can detect and recognize objects in a visual input in real-time. This is achieved using convolutional neural networks that can predict different bounding boxes and class probabilities simultaneously.
As its name implies, YOLO can detect objects by passing an image through a neural network only once. The algorithm completes the prediction for an entire image within one algorithm run. It is also capable of ‘learning’ new things quickly and effectively, storing data on object representations and leveraging this information for object detection.
Enforcing social distancing measures during the height of the COVID-19 pandemic was critical yet extremely difficult for jurisdictions with limited resources and large populations. To address this issue, authorities in some parts of the world adopted computer vision solutions such as YOLO to develop social distancing tools.
YOLO can track people within a specific geographical area and judge whether social distancing norms are being followed. It applies object detection and tracking principles in real-time to detect social distancing violations and alert the relevant authorities.
In practice, YOLO works by capturing each person present in the visual input by using bounding boxes. The movement of these boxes is tracked within the frame, and the distance among them is constantly recalculated. If a violation of social distancing guidelines is detected, the algorithm highlights the offending bounding boxes and enables further actions to be triggered.
Faceapp is a popular image manipulation application that modifies visual inputs of human faces to change gender, age, and other features. This is achieved through deep convolutional generative adversarial networks, a specific subtype of computer vision.
Faceapp combines image recognition principles, a key aspect of facial recognition, with deep learning to recognize key facial features such as cheekbones, eyelids, nose bridge, and jawline. Once these features are outlined on the human face, the app can modify them to transform the image.
Faceapp works by collecting sample data from the smartphones of multiple users and feeding it to the deep neural networks. This allows the system to ‘learn’ every small detail of the appearance of the human face. These learnings are then used to bolster the app’s predictive ability and enable it to simulate wrinkles, modify hairlines, and make other realistic changes to images of the human face.
Faceapp relies on computer vision to recognize patterns. Its artificial intelligence capabilities have enabled it to imitate images with increasing efficiency over time, using the data it receives from numerous sources. Faceapp transfers facial information from one picture to another at the micro-level. This leads to impressive capabilities at the macro level, consequently allowing the app to create a large database by processing millions of user photos.
SentioScope is a fitness and sports tracking system developed by Sentio. It primarily operates as a player tracking solution for soccer, processing real-time visual inputs from live games. Recorded data is uploaded to cloud-based analytical platforms.
SentioScope relies on a 4K camera setup to capture visual inputs. It then processes these inputs to detect players and gain real-time insights from their movement and behavior.
This computer vision-powered solution creates a conceptual model of the soccer field, representing the game in a two-dimensional world. This 2D model is partitioned into a grid of dense spatial cells. Each cell represents a unique ground point on the field, shown as a fixed image patch in the video.
SentioScope is powered by machine learning and trained with more than 100,000 player samples. This enables it to detect ‘player’ cells in the footage of soccer games. The probabilistic algorithm can function in numerous types of challenging visibility conditions.
Sentio is one of the many companies working to infuse computer vision with sports training regimens. These solutions usually analyze live feeds from high-resolution cameras to track moving balls, detect player positions, and record other useful information that one can use to enhance player and team performance.
See More: Top 10 Python Libraries for Machine Learning
Although the capabilities of the human eyes are beyond incredible, present-day computer vision is working hard to catch up. Listed below are the top 10 applications of computer vision in 2022.
Agriculture is not traditionally associated with cutting-edge technology. However, outdated methodologies and tools are slowly being phased out from farmlands worldwide. Today, farmers are leveraging computer vision to enhance agricultural productivity.
Companies specializing in agriculture technology are developing advanced computer vision and artificial intelligence models for sowing and harvesting purposes. These solutions are also useful for weeding, detecting plant health, and advanced weather analysis.
Computer vision has numerous existing and upcoming applications in agriculture, including drone-based crop monitoring, automatic spraying of pesticides, yield tracking, and smart crop sorting & classification. These AI-powered solutions scan the crops’ shape, color, and texture for further analysis. Through computer vision technology, weather records, forestry data, and field security are also increasingly used.
2022 is the year of self-driving cars. Market leaders such as Tesla, backed by advanced technologies such as computer vision and 5G, are making great strides.
Tesla’s autonomous cars use multi-camera setups to analyze their surroundings. This enables the vehicles to provide users with advanced features, such as autopilot. The vehicle also uses 360-degree cameras to detect and classify objects through computer vision.
Drivers of autonomous cars can either drive manually or allow the vehicle to make autonomous decisions. In case a user chooses to go with the latter arrangement, these vehicles use computer vision to engage in advanced processes such as path planning, driving scene perception, and behavior arbitration.
While facial recognition is already in use at the personal level, such as through smartphone applications, the public security industry is also a noteworthy driver of facial detection solutions. Detecting and recognizing faces in public is a contentious application of computer vision that is already being implemented in certain jurisdictions and banned in others.
Successful facial detection relies on deep learning and machine vision. Computer vision algorithms detect and capture images of people’s faces in public. This data is then sent to the backend system for analysis. A typical facial recognition solution for large-scale public use combines analysis and recognition algorithms.
Proponents support computer vision-powered facial recognition because it can be useful for detecting and preventing criminal activities. These solutions also have applications in tracking specific persons for security missions.
Human pose tracking models use computer vision to process visual inputs and estimate human posture. Tracking human poses is another capability of computer vision applied in industries such as gaming, robotics, fitness apps, and physical therapy. 
For instance, the Microsoft Kinect gaming device can accurately monitor player actions through the use of AI vision. It works by detecting the positions of human skeletal joints on a 3D plane and recognizing their movements.
Gone are the days when digital entertainment meant that the viewer had to sit and watch without participating. Today, interactive entertainment solutions leverage computer vision to deliver truly immersive experiences. Cutting-edge entertainment services use artificial intelligence to allow users to partake in dynamic experiences.
For instance, Google Glass and other smart eyewear demonstrate how users can receive information about what they see while looking at it. The information is directly sent to the user’s field of vision. These devices can also respond to head movements and changes in expressions, enabling users to transmit commands simply by moving their heads.
Medical systems rely heavily on pattern detection and image classification principles for diagnoses. While these activities were largely carried out manually by qualified healthcare professionals, computer vision solutions are slowly stepping up to help doctors diagnose medical conditions.
There has been a noteworthy increase in the application of computer vision techniques for the processing of medical imagery. This is especially prevalent in pathology, radiology, and ophthalmology. Visual pattern recognition, through computer vision, enables advanced products, such as Microsoft InnerEye, to deliver swift and accurate diagnoses in an increasing number of medical specialties.
Manufacturing is one of the most technology-intensive processes in the modern world. Computer vision is popular in manufacturing plants and is commonly used in AI-powered inspection systems. Such systems are prevalent in R&D laboratories and warehouses and enable these facilities to operate more intelligently and effectively.
For instance, predictive maintenance systems use computer vision in their inspection systems. These tools minimize machinery breakdowns and product deformities by constantly scanning the environment. If a likely breakdown or low-quality product is detected, the system notifies human personnel, allowing them to trigger further actions. Apart from this, computer vision is used by workers in packaging and quality monitoring activities.
Thanks to advancements brought about by Industry 4.0, computer vision is also being used to automate otherwise labor-intensive processes such as product assembly and management. AI-powered product assembly is most commonly seen in assembly lines for delicate commodities, such as electronics. Companies such as Tesla are bringing about the complete automation of manufacturing processes in their plants.
While interaction-free shopping experiences were always the inevitable future, the COVID-19 pandemic certainly helped speed up the retail industry’s adoption of computer vision applications. Today, tech giants such as Amazon are actively exploring how retail can be revolutionized using AI vision to allow customers to ‘take and leave’.
Retail stores are already embracing computer vision solutions to monitor shopper activity, making loss prevention non-intrusive and customer-friendly. Computer vision is also being used to analyze customer moods and personalize advertisements. Apart from this, AI-driven vision solutions are being used to maximize ROI through customer retention programs, inventory tracking, and the assessment of product placement strategies.
With remote education receiving a leg-up due to the COVID-19 pandemic, the education technology industry is also leveraging computer vision for various applications. For instance, teachers use computer vision solutions to evaluate the learning process non-obstructively. These solutions allow teachers to identify disengaged students and tweak the teaching process to ensure that they are not left behind.
Apart from this, AI vision is being used for applications such as school logistic support, knowledge acquisition, attendance monitoring, and regular assessments. One common example of this is computer vision-enabled webcams, which are being used to monitor students during examinations. This makes unfair practices easier to spot through the analysis of eye movements and body behavior.
Finally, computer vision systems are being increasingly applied to increase transportation efficiency. For instance, computer vision is being used to detect traffic signal violators, thus allowing law enforcement agencies to minimize unsafe on-road behavior.
Intelligent sensing and processing solutions are also being used to detect speeding and wrong‐side driving violations, among other disruptive behaviors. Apart from this, computer vision is being used by intelligent transportation systems for traffic flow analysis.
See More: What Are the Types of Artificial Intelligence: Narrow, General, and Super AI Explained
Computer vision is a groundbreaking technology with many exciting applications. This cutting-edge solution uses the data that we generate every day to help computers ‘see’ our world and give us useful insights that will help increase the overall quality of life. In 2022, computer vision is expected to unlock the potential of many new and exciting technologies, helping us lead safer, healthier, and happier lives.
Did you gain a comprehensive understanding of computer vision through this article? Share your thoughts with us on LinkedIn, Twitter, or Facebook! We’d love to hear from you.

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