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What is Computer Vision and its Benefits - Rishabh Software

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What is computer vision? Explore computer vision benefits, how it works, types, libraries & frameworks and how we can help build robust computer vision solutions.

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Computer vision (CV) is the subcategory of artificial intelligence ( AI) that focuses on building and using digital systems to process, analyze and interpret visual data. The goal of computer vision is to enable computing devices to correctly identify an object or person in a digital image and take appropriate action.

Computer vision uses convolutional neural networks ( CNNs) to processes visual data at the pixel level and deep learning recurrent neural network ( RNNs) to understand how one pixel relates to another.

Uses for computer vision include:

Biometric access management - CV plays an important role in both facial and iris recognition.

Industrial robots and self-driving cars - CV allows robots and autonomous vehicles to avoid collisions and navigate safely.

Digital diagnostics - CV can be used in tandem with other types of artificial intelligence programming to automate the analysis of X-rays and MRIs.

Augmented reality - CV allows mixed reality programming to know where a virtual object should be placed.

Techopedia Explains Computer Vision

Computer vision includes image processing as well as pattern recognition and object detection. It has a wide range of practical applications, from self-driving cars and facial recognition technology to medical imaging and augmented reality.
Computer Vision and Self-Driving Cars
Here are some ways that computer vision is used in self-driving cars:

Allow self-driving cars to identify and distinguish objects such as traffic lights, pedestrians and road signs.

Detect and track the edges of the road.

Plan a path through traffic by predicting the motion of other objects in the environment.

Computer Vision and Facial Recognition
Here are some ways that computer vision is used in facial recognition:

Identify specific facial features and use this information to determine whether a face is present.

Extract a set of unique facial features such as the distance between the eyes or the shape of the nose to create a template that represents a specific individual's face.

Match the template with a single image in the database.

Computer Vision and Medical Imaging
Here are some ways that computer vision is used in medical imaging:

Segment medical images into different regions of interest, such as organs or tissues.

Remove noise and enhance image contrast to make it easier for doctors to identify abnormalities.

Support computer-aided diagnosis systems that analyze medical images and provide physicians with diagnostic support.

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This definition explains the meaning of Computer Vision and why it matters.

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Accelerate the Development of AI Applications | Scale AI

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Today, computer vision is one of the most important subfields of Artificial Intelligence and machine learning, given its wide variety of applications and tremendous potential. It aims to replicate the powerful capabilities of human vision.

But what exactly is computer vision? What are the typical tasks of computer vision? How is it currently applied in different industries? What is the important role of Theos AI in relation to computer vision?

What is Computer Vision?

﻿Computer vision is the field aiming at giving machines the power to analyze and understand images and videos. While the types of algorithms that make this possible have existed in various forms since the 1960s, recent advances in machine learning and computational power, along with the accumulation of high amounts of image data, have driven significant improvements in how well our machines can see the world.

Typical Tasks in Computer Vision

Computer vision is composed of a broad set of diverse tasks, combined to achieve highly sophisticated applications. The most common tasks in computer vision are image classification, object detection, semantic and instance segmentation, pose estimation and face recognition. Some of the newest tasks include image inpainting, face generation, and text to image generation. The latter is the task performed by DALLE-2, the incredible AI artist created by OpenAI this year.

What is Image Classification?

Image Classification is a fundamental computer vision task that attempts to comprehend an entire image as a whole. The goal is to classify the image by assigning it to a specific label.

Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.

Industry Applications

Humans are not only capable of understanding scenes, but also of interpreting calligraphy, impressionistic or abstract paintings and, with a little training, the 2D ultrasound of a baby. In that sense, the field of computer vision is particularly complex, possessing an immense range of practical applications. The beauty of innovation that relies on artificial intelligence, and computer vision in particular, is that companies of all types and sizes, from the e-commerce industry to the more classical ones like manufacturing, can take advantage of its powerful capabilities.

Let's take a look at some of the industries that have been the most impacted by computer vision.

Manufacturing

The two main problems that can occur on a manufacturing line are machine breakdowns or the production of defective products. This results in delays and significant losses in profits.

Machine vision algorithms prove to be a great means of automatic maintenance. By analyzing visual information (e.g., video from cameras attached to robots), algorithms can identify potential problems before they become severe. The fact that a system can anticipate that an automotive packaging or assembly robot will fail is a great contribution.

The same idea applies to defect reduction, where the system can detect defects in components and products along the entire production line. This allows manufacturers to take action in real-time and decide what needs to be done in order to solve the problem. Perhaps the defect is not so serious and the process can continue, but the product is marked in some way or rerouted through a specific production route. Sometimes, however, it may be necessary to stop the production line. Another interest is that the system can be trained, for each use case, to classify defects by types and degrees of severity.

Healthcare

In healthcare, the number of existing machine vision applications are outstanding.

Undoubtedly, medical image analysis is the best known example, as it helps to significantly improve the medical diagnostic process. Images from MRIs, CT scans and X-rays are analyzed to find abnormalities such as tumors or to look for signs of neurological diseases.

Autonomous vehicles

Have you ever wondered how autonomous cars can "see" the world? The field of computer vision plays a central role in the domain of autonomous vehicles, as it allows them to perceive and understand the environment around them in order to operate correctly.

One of the most exciting challenges in computer vision is the detection of objects in images and videos. This involves locating a variable number of objects and the ability to classify them, to distinguish whether an object is a traffic light, a car or a person.

This type of technology, combined with data analysis from other sources, such as sensors and/or radar, is what allows an autonomous vehicle to perceive the world.

Insurance

The use of computer vision in insurance has had a major impact, particularly in claims processing.

A computer vision application can guide customers through the process of visually documenting a claim. It can analyze images in real-time and send them to the appropriate agents. At the same time, it can estimate and adjust repair costs, determine if they are covered by insurance, and even check for possible fraud. All of this minimizes the claims cycle time, resulting in a better customer experience.

From a preventive standpoint, computer vision is of great help in avoiding accidents; there are applications for collision prevention, integrated into industrial machinery, automobiles and drones. This is a new era of risk management that will most likely change the field of insurance forever.

Agriculture

Agriculture is an important industry where computer vision is having a tremendous impact, especially in the area of precision agriculture.

In grain production, a global economic activity, a number of valuable applications have been developed. Grain production faces certain recurring problems, which have historically been monitored by humans. However, computer vision algorithms can now detect, or in some cases can reasonably predict, pest and insect diseases or infestations. Early diagnosis allows growers to take appropriate action quickly, reducing losses and ensuring yield quality.

Another ongoing challenge is weed control, considering that weeds have become resistant to herbicides over time and represent significant losses for farmers. There are robots with integrated machine vision technology that monitor entire farms and spray herbicides with precision. This saves huge volumes of pesticides, which is an incredible benefit for the planet and in terms of production costs.

Soil quality is also an important factor in agriculture. There are applications that can recognize, from images taken with cell phones, possible defects and nutritional deficiencies in soils. After analyzing the images sent, these applications suggest soil restoration techniques and possible solutions to the problems detected.

Computer vision can be further used in grading. Algorithms exist to sort fruits, vegetables and even flowers by identifying their main properties (e.g., size, quality, weight, color, texture). These algorithms are also able to detect defects and estimate which items will last longer and which should be sent to local markets. This leads to maximizing the shelf life of the items and reduces their time to market.

All of these examples can be made possible thanks to Theos. Our AI development platform will eventually support all the subfields of AI that mimic most brain functions, such as computer vision, natural language processing, and speech recognition and synthesis. For now, we have object detection development, the task of giving an image to an AI and asking it to predict the classes of objects within the image and their positions.

You can take a look at our platform and learn more about it. You will be to create your own artificial intelligence without the need to be an expert engineer in the subject.

You can try it now for free.

Today, computer vision is one of the most important subfields of Artificial Intelligence and machine learning, given its wide variety of applications and tremendous potential. It aims to replicate the powerful capabilities of human vision.

But what exactly is computer vision? What are the typical tasks of computer vision? How is it currently applied in different industries? What is the important role of Theos AI in relation to computer vision?

What is Computer Vision?

﻿Computer vision is the field aiming at giving machines the power to analyze and understand images and videos. While the types of algorithms that make this possible have existed in various forms since the 1960s, recent advances in machine learning and computational power, along with the accumulation of high amounts of image data, have driven significant improvements in how well our machines can see the world.

Typical Tasks in Computer Vision

Computer vision is composed of a broad set of diverse tasks, combined to achieve highly sophisticated applications. The most common tasks in computer vision are image classification, object detection, semantic and instance segmentation, pose estimation and face recognition. Some of the newest tasks include image inpainting, face generation, and text to image generation. The latter is the task performed by DALLE-2, the incredible AI artist created by OpenAI this year.

What is Image Classification?

Image Classification is a fundamental computer vision task that attempts to comprehend an entire image as a whole. The goal is to classify the image by assigning it to a specific label.

Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.

Industry Applications

Humans are not only capable of understanding scenes, but also of interpreting calligraphy, impressionistic or abstract paintings and, with a little training, the 2D ultrasound of a baby. In that sense, the field of computer vision is particularly complex, possessing an immense range of practical applications. The beauty of innovation that relies on artificial intelligence, and computer vision in particular, is that companies of all types and sizes, from the e-commerce industry to the more classical ones like manufacturing, can take advantage of its powerful capabilities.

Let's take a look at some of the industries that have been the most impacted by computer vision.

Manufacturing

The two main problems that can occur on a manufacturing line are machine breakdowns or the production of defective products. This results in delays and significant losses in profits.

Machine vision algorithms prove to be a great means of automatic maintenance. By analyzing visual information (e.g., video from cameras attached to robots), algorithms can identify potential problems before they become severe. The fact that a system can anticipate that an automotive packaging or assembly robot will fail is a great contribution.

The same idea applies to defect reduction, where the system can detect defects in components and products along the entire production line. This allows manufacturers to take action in real-time and decide what needs to be done in order to solve the problem. Perhaps the defect is not so serious and the process can continue, but the product is marked in some way or rerouted through a specific production route. Sometimes, however, it may be necessary to stop the production line. Another interest is that the system can be trained, for each use case, to classify defects by types and degrees of severity.

Healthcare

In healthcare, the number of existing machine vision applications are outstanding.

Undoubtedly, medical image analysis is the best known example, as it helps to significantly improve the medical diagnostic process. Images from MRIs, CT scans and X-rays are analyzed to find abnormalities such as tumors or to look for signs of neurological diseases.

Autonomous vehicles

Have you ever wondered how autonomous cars can "see" the world? The field of computer vision plays a central role in the domain of autonomous vehicles, as it allows them to perceive and understand the environment around them in order to operate correctly.

One of the most exciting challenges in computer vision is the detection of objects in images and videos. This involves locating a variable number of objects and the ability to classify them, to distinguish whether an object is a traffic light, a car or a person.

This type of technology, combined with data analysis from other sources, such as sensors and/or radar, is what allows an autonomous vehicle to perceive the world.

Insurance

The use of computer vision in insurance has had a major impact, particularly in claims processing.

A computer vision application can guide customers through the process of visually documenting a claim. It can analyze images in real-time and send them to the appropriate agents. At the same time, it can estimate and adjust repair costs, determine if they are covered by insurance, and even check for possible fraud. All of this minimizes the claims cycle time, resulting in a better customer experience.

From a preventive standpoint, computer vision is of great help in avoiding accidents; there are applications for collision prevention, integrated into industrial machinery, automobiles and drones. This is a new era of risk management that will most likely change the field of insurance forever.

Agriculture

Agriculture is an important industry where computer vision is having a tremendous impact, especially in the area of precision agriculture.

In grain production, a global economic activity, a number of valuable applications have been developed. Grain production faces certain recurring problems, which have historically been monitored by humans. However, computer vision algorithms can now detect, or in some cases can reasonably predict, pest and insect diseases or infestations. Early diagnosis allows growers to take appropriate action quickly, reducing losses and ensuring yield quality.

Another ongoing challenge is weed control, considering that weeds have become resistant to herbicides over time and represent significant losses for farmers. There are robots with integrated machine vision technology that monitor entire farms and spray herbicides with precision. This saves huge volumes of pesticides, which is an incredible benefit for the planet and in terms of production costs.

Soil quality is also an important factor in agriculture. There are applications that can recognize, from images taken with cell phones, possible defects and nutritional deficiencies in soils. After analyzing the images sent, these applications suggest soil restoration techniques and possible solutions to the problems detected.

Computer vision can be further used in grading. Algorithms exist to sort fruits, vegetables and even flowers by identifying their main properties (e.g., size, quality, weight, color, texture). These algorithms are also able to detect defects and estimate which items will last longer and which should be sent to local markets. This leads to maximizing the shelf life of the items and reduces their time to market.

All of these examples can be made possible thanks to Theos. Our AI development platform will eventually support all the subfields of AI that mimic most brain functions, such as computer vision, natural language processing, and speech recognition and synthesis. For now, we have object detection development, the task of giving an image to an AI and asking it to predict the classes of objects within the image and their positions.

You can take a look at our platform and learn more about it. You will be to create your own artificial intelligence without the need to be an expert engineer in the subject.

You can try it now for free.

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Introduction to Computer Vision (Ultimate Guide 2023) - Theos AI

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Computer vision is one of the most important subfields of artificial intelligence and machine learning, given its wide variety of applications and tremendous potential. It aims to replicate the powerful capabilities of human vision.

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Computer vision is a field of artificial intelligence (AI) that applies machine learning to images and videos to understand media and make decisions about them. With computer vision, we can, in a sense, give vision to software and technology.

How Does Computer Vision Work?

Computer vision programs use a combination of techniques to process raw images and turn them into usable data and insights.

The basis for much computer vision work is 2D images, as shown below. While images may seem like a complex input, we can decompose them into raw numbers. Images are really just a combination of individual pixels and each pixel can be represented by a number (grayscale) or combination of numbers such as (255, 0, 0- RGB).

The Built In favicon (left) shown in grayscale and (right) the same image with the pixel values overlaid. | Image: Jye Sawtell-Rickson.

Once we've translated an image to a set of numbers, a computer vision algorithm applies processing. One way to do this is a classic technique called convolutional neural networks (CNNs) that uses layers to group together the pixels in order to create successively more meaningful representations of the data. A CNN may first translate pixels into lines, which are then combined to form features such as eyes and finally combined to create more complex items such as face shapes.

Why Is Computer Vision Important?

Computer vision has been around since as early as the 1950s and continues to be a popular field of research with many applications. According to the deep learning research group, BitRefine, we should expect the computer vision industry to grow to nearly 50 billion USD in 2022, with 75 percent of the revenue deriving from hardware.

The importance of computer vision comes from the increasing need for computers to be able to understand the human environment. To understand the environment, it helps if computers can see what we do, which means mimicking the sense of human vision. This is especially important as we develop more complex AI systems that are more human-like in their abilities.

On That Note. . . How Do Self-Driving Cars Work?

Computer Vision Examples

Computer vision is often used in everyday life and its applications range from simple to very complex.

Optical character recognition (OCR) was one of the most widespread applications of computer vision. The most well-known case of this today is Google's Translate, which can take an image of anything - from menus to signboards - and convert it into text that the program then translates into the user's native language. We can also apply OCR in other use cases such as automated tolling of cars on highways and translating hand-written documents into digital counterparts.

A more recent application, which is still under development and will play a big role in the future of transportation, is object recognition. In object recognition an algorithm takes an input image and searches for a set of objects within the image, drawing boundaries around the object and labelling it. This application is critical in self-driving cars which need to quickly identify its surroundings in order to decide on the best course of action.

Computer Vision Applications

What Are the Risks of Computer Vision?

As with all technology, computer vision is a tool, which means that it can have benefits, but also risks. Computer vision has many applications in everyday life that make it a useful part of modern society but recent concerns have been raised around privacy. The issue that we see most often in the media is around facial recognition. Facial recognition technology uses computer vision to identify specific people in photos and videos. In its lightest form it's used by companies such as Meta or Google to suggest people to tag in photos, but it can also be used by law enforcement agencies to track suspicious individuals. Some people feel facial recognition violates privacy, especially when private companies may use it to track customers to learn their movements and buying patterns.

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What Is Computer Vision? (Definition, Examples, Uses) | Built In

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Computer vision is a field of artificial intelligence (AI) that applies machine learning to images and videos to understand media and make decisions about them.

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Ubicept wants half of the world's cameras to see things differently

Computer vision could be a lot faster and better if we skip the concept of still frames and instead directly analyze the data stream from a camera. At least, that's the theory that the newest brainchild spinning out of the MIT Media lab, Ubicept, is operating under.

Most computer vision applications work the same way: A camera takes an image (or a rapid series of images, in the case of video). These still frames are passed to a computer, which then does the analysis to figure out what is in the image. Sounds simple enough.

But there's a problem: That paradigm assumes that creating still frames is a good idea. As humans who are used to seeing photography and video, that might seem reasonable. Computers don't care, however, and Ubicept believes it can make computer vision far better and more reliable by ignoring the idea of frames.

The company itself is a collaboration between its co-founders. Sebastian Bauer is the company's CEO and a postdoc at the University of Wisconsin, where he was working on lidar systems. Tristan Swedish is now Ubicept's CTO. Before that, he was a research assistant and a master's and Ph.D. student at the MIT Media Lab for eight years.

"There are 45 billion cameras in the world, and most of them are creating images and video that aren't really being looked at by a human," Bauer explained. "These cameras are mostly for perception, for systems to make decisions based on that perception. Think about autonomous driving, for example, as a system where it is about pedestrian recognition. There are all these studies coming out that show that pedestrian detection works great in bright daylight but particularly badly in low light. Other examples are cameras for industrial sorting, inspection and quality assurance. All these cameras are being used for automated decision-making. In sufficiently lit rooms or in daylight, they work well. But in low light, especially in connection with fast motion, problems come up."

The company's solution is to bypass the "still frame" as the source of truth for computer vision and instead measure the individual photons that hit an imaging sensor directly. That can be done with a single-photon avalanche diode array (or SPAD array, among friends). This raw stream of data can then be fed into a field-programmable gate array (FPGA, a type of super-specialized processor) and further analyzed by computer vision algorithms.

The newly founded company demonstrated its tech at CES in Las Vegas in January, and it has some pretty bold plans for the future of computer vision.

"Our vision is to have technology on at least 10% of cameras in the next five years, and in at least 50% of cameras in the next 10 years," Bauer projected. "When you detect each individual photon with a very high time resolution, you're doing the best that nature allows you to do. And you see the benefits, like the high-quality videos on our webpage, which are just blowing everything else out of the water."

TechCrunch saw the technology in action at a recent demonstration in Boston and wanted to explore how the tech works and what the implications are for computer vision and AI applications.

A new form of seeing

Digital cameras generally work by grabbing a single-frame exposure by "counting" the number of photons that hit each of the sensor pixels over a certain period of time. At the end of the time period, all of those photons are multiplied together, and you have a still photograph. If nothing in the image moves, that works great, but the "if nothing moves" thing is a pretty big caveat, especially when it comes to computer vision. It turns out that when you are trying to use cameras to make decisions, everything moves all the time.

Of course, with the raw data, the company is still able to combine the stream of photons into frames, which creates beautifully crisp video without motion blur. Perhaps more excitingly, dispensing with the idea of frames means that the Ubicept team was able to take the raw data and analyze it directly. Here's a sample video of the dramatic difference that can make in practice:

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Everything you know about computer vision may soon be wrong | TechCrunch

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Ubicept believes it can make computer vision far better and more reliable by ignoring the idea of frames.

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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.
What Is 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'.

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.

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.
See More: What Is Artificial Intelligence: History, Types, Applications, Benefits, Challenges, and Future of AI
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.

5 Examples of Computer Vision

1. Google Translate

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.

2. Facebook 3D Photo

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 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.

5. SentioScope

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.
See More: Top 10 Python Libraries for Machine Learning
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.

Top 10 Applications of Computer Vision in 2022

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.

1. Agriculture

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.

2. Autonomous vehicles

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.

3. Facial recognition

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.

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.

4. Human pose tracking

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.

5. Interactive entertainment

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.

6. Medical imaging

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.

7. Manufacturing

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.

8. Retail management

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'.

9. Education

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.
See More: What Are the Types of Artificial Intelligence: Narrow, General, and Super AI Explained
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.

10. Transportation

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.

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.

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Computer Vision Meaning, Examples, Applications

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Computer vision is the process of using artificial intelligence to enable computers to obtain meaningful data from visual inputs.

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Computer vision is a subfield of artificial intelligence that deals with acquiring, processing, analyzing, and making sense of visual data such as digital images and videos. It is one of the most compelling types of artificial intelligence that we regularly implement in our daily routines.

Computer vision helps to understand the complexity of the human vision system and trains computer systems to interpret and gain a high-level understanding of digital images or videos. In the early days, developing a machine system having human-like intelligence was just a dream, but with the advancement of artificial intelligence and machine learning, it also became possible. Similarly, such intelligent systems have been developed that can "see" and interpret the world around them, similar to human eyes. The fiction of yesterday has become the fact of today. In this tutorial, "Computer Vision Introduction", we will discuss a few important concepts of computer vision, such as:

What is Computer Vision?

How does Computer Vision Work?

The evolution of computer vision

Applications of computer vision

Challenges of computer vision

What is Computer Vision?

Computer vision is one of the most important fields of artificial intelligence (AI) and computer science engineering that makes computer systems capable of extracting meaningful information from visual data like videos and images. Further, it also helps to take appropriate actions and make recommendations based on the extracted information.

Further, Artificial intelligence is the branch of computer science that primarily deals with creating a smart and intelligent system that can behave and think like the human brain. So, we can say if artificial intelligence enables computer systems to think intelligently, computer vision makes them capable of seeing, analyzing, and understanding.

History of Computer Vision

Computer vision is not a new technology because scientists and experts have been trying to develop machines that can see and understand visual data for almost six decades. The evolution of computer vision is classified as follows:

1959: The first experiment with computer vision was initiated in 1959, where they showed a cat as an array of images. Initially, they found that the system reacts first to hard edges or lines, and scientifically, this means that image processing begins with simple shapes such as straight edges.

1960: In 1960, artificial intelligence was added as a field of academic study to solve human vision problems.

1963: This was another great achievement for scientists when they developed computers that could transform 2D images into 3-D images.

1974: This year, optical character recognition (OCR) and intelligent character recognition (ICR) technologies were successfully discovered. The OCR has solved the problem of recognizing text printed in any font or typeface, whereas ICR can decrypt handwritten text. These inventions are one of the greatest achievements in document and invoice processing, vehicle number plate recognition, mobile payments, machine translation, etc.

1982: In this year, the algorithm was developed to detect edges, corners, curves, and other shapes. Further, scientists also developed a network of cells that could recognize patterns.

2000: In this year, scientists worked on a study of object recognition.

2001: The first real-time face recognition application was developed.

2010: The ImageNet data set became available to use with millions of tagged images, which can be considered the foundation for recent Convolutional Neural Network (CNN) and deep learning models.

2012: CNN has been used as an image recognition technology with a reduced error rate.

2014: COCO has also been developed to offer a dataset for object detection and support future research.

How does Computer Vision Work?

Computer vision is a technique that extracts information from visual data, such as images and videos. Although computer vision works similarly to human eyes with brain work, this is probably one of the biggest open questions for IT professionals: How does the human brain operate and solve visual object recognition?

On a certain level, computer vision is all about pattern recognition which includes the training process of machine systems for understanding the visual data such as images and videos, etc.

Firstly, a vast amount of visual labeled data is provided to machines to train it. This labeled data enables the machine to analyze different patterns in all the data points and can relate to those labels. E.g., suppose we provide visual data of millions of dog images. In that case, the computer learns from this data, analyzes each photo, shape, the distance between each shape, color, etc., and hence identifies patterns similar to dogs and generates a model. As a result, this computer vision model can now accurately detect whether the image contains a dog or not for each input image.

Task Associated with Computer Vision

Although computer vision has been utilized in so many fields, there are a few common tasks for computer vision systems. These tasks are given below:

Object classification: Object classification is a computer vision technique/task used to classify an image, such as whether an image contains a dog, a person's face, or a banana. It analyzes the visual content (videos & images) and classifies the object into the defined category. It means that we can accurately predict the class of an object present in an image with image classification.

Object Identification/detection: Object identification or detection uses image classification to identify and locate the objects in an image or video. With such detection and identification technique, the system can count objects in a given image or scene and determine their accurate location and labeling. For example, in a given image, one dog, one cat, and one duck can be easily detected and classified using the object detection technique.

Object Verification: The system processes videos, finds the objects based on search criteria, and tracks their movement.

Object Landmark Detection: The system defines the key points for the given object in the image data.

Image Segmentation: Image segmentation not only detects the classes in an image as image classification; instead, it classifies each pixel of an image to specify what objects it has. It tries to determine the role of each pixel in the image.

Object Recognition: In this, the system recognizes the object's location with respect to the image.

How to learn computer Vision?

Although, computer vision requires all basic concepts of machine learning, deep learning, and artificial intelligence. But if you are eager to learn computer vision, then you must follow below things, which are as follows:

1. Build your foundation:

Before entering this field, you must have strong knowledge of advanced mathematical concepts such as Probability, statistics, linear algebra, calculus, etc.

The knowledge of programming languages like Python would be an extra advantage to getting started with this domain.

2. Digital Image Processing:
It would be best if you understood image editing tools and their functions, such as histogram equalization, median filtering, etc. Further, you should also know about compressing images and videos using JPEG and MPEG files. Once you know the basics of image processing and restoration, you can kick-start your journey into this domain.

3. Machine learning understanding
To enter this domain, you must deeply understand basic machine learning concepts such as CNN, neural networks, SVM, recurrent neural networks, generative adversarial neural networks, etc.

4. Basic computer vision: This is the step where you need to decrypt the mathematical models used in visual data formulation.

These are a few important prerequisites that are essentially required to start your career in computer vision technology. Once you are prepared with the above prerequisites, you can easily start learning and make a career in Computer vision.

Applications of computer vision

Computer vision is one of the most advanced innovations of artificial intelligence and machine learning. As per the increasing demand for AI and Machine Learning technologies, computer vision has also become a center of attraction among different sectors. It greatly impacts different industries, including retail, security, healthcare, automotive, agriculture, etc.

Below are some most popular applications of computer vision:

Facial recognition: Computer vision has enabled machines to detect face images of people to verify their identity. Initially, the machines are given input data images in which computer vision algorithms detect facial features and compare them with databases of fake profiles. Popular social media platforms like Facebook also use facial recognition to detect and tag users. Further, various government spy agencies are employing this feature to identify criminals in video feeds.

Healthcare and Medicine: Computer vision has played an important role in the healthcare and medicine industry. Traditional approaches for evaluating cancerous tumors are time-consuming and have less accurate predictions, whereas computer vision technology provides faster and more accurate chemotherapy response assessments; doctors can identify cancer patients who need faster surgery with life-saving precision.

Self-driving vehicles: Computer vision technology has also contributed to its role in self-driving vehicles to make sense of their surroundings by capturing video from different angles around the car and then introducing it into the software. This helps to detect other cars and objects, read traffic signals, pedestrian paths, etc., and safely drive its passengers to their destination.

Optical character recognition (OCR)
Optical character recognition helps us extract printed or handwritten text from visual data such as images. Further, it also enables us to extract text from documents like invoices, bills, articles, etc.

Machine inspection: Computer vision is vital in providing an image-based automatic inspection. It detects a machine's defects, features, and functional flaws, determines inspection goals, chooses lighting and material-handling techniques, and other irregularities in manufactured products.

Retail (e.g., automated checkouts): Computer vision is also being implemented in the retail industries to track products, shelves, wages, record product movements into the store, etc. This AI-based computer vision technique automatically charges the customer for the marked products upon checkout from the retail stores.

3D model building: 3D model building or 3D modeling is a technique to generate a 3D digital representation of any object or surface using the software. In this field also, computer vision plays its role in constructing 3D computer models from existing objects. Furthermore, 3D modeling has a variety of applications in various places, such as Robotics, Autonomous driving, 3D tracking, 3D scene reconstruction, and AR/VR.

Medical imaging: Computer vision helps medical professionals make better decisions regarding treating patients by developing visualization of specific body parts such as organs and tissues. It helps them get more accurate diagnoses and a better patient care system. E.g., Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scanner to diagnose pathologies or guide medical interventions such as surgical planning or for research purposes.

Automotive safety: Computer vision has added an important safety feature in automotive industries. E.g., if a vehicle is taught to detect objects and dangers, it could prevent an accident and save thousands of lives and property.

Surveillance: It is one of computer vision technology's most important and beneficial use cases. Nowadays, CCTV cameras are almost fitted in every place, such as streets, roads, highways, shops, stores, etc., to spot various doubtful or criminal activities. It helps provide live footage of public places to identify suspicious behavior, identify dangerous objects, and prevent crimes by maintaining law and order.

Fingerprint recognition and biometrics: Computer vision technology detects fingerprints and biometrics to validate a user's identity. Biometrics deals with recognizing persons based on physiological characteristics, such as the face, fingerprint, vascular pattern, or iris, and behavioral traits, such as gait or speech. It combines Computer Vision with knowledge of human physiology and behavior.

How to become a computer vision engineer?

Computer vision is one of the world's most popular & high-demand technologies. Although starting your career in this domain is not easy, if you have a good command of machine learning basics, advanced mathematics concepts, and the basics of computer vision, you can easily start your career as a computer vision engineer.

There are some roles and responsibilities required to become a computer vision engineer, which is as follows

To create and implement a vision algorithm for working with image and video content pixels

To develop a data-based approach for better problem solutions.

Whenever required, you have to work on various AI and ML tasks required for computer vision, such as image processing.

Experience in working on various real-time project scenarios for problem-solving.

Hierarchical problem decomposition, implementation of solutions, and integration with other sub-systems.

Hierarchical problem decomposition, implementation of solutions, and integration with other sub-systems.

Should be capable of understanding business objectives and can connect to technical solutions through effective system design and architecture.

Job description (JD) for Computer vision engineer

The candidate must have cumulative work experience in visual data processing and analysis using machine learning and deep learning.

Hands-on experience with various AI/ML frameworks such as Python, TensorFlow, PyTorch, Keras, CPP, etc.

Candidates must have good experience in implementing AI techniques.

Must have good written and verbal communication skills.

Candidates should be aware of object detection techniques and models such as YOLO, RCNN, etc.

Which programming language is best for computer vision?

Computer vision engineers require in-depth knowledge of machine learning and deep learning concepts with strong command over at least one programming language. There are so many programming languages that can be used in this domain, but Python is among the most popular. However, one can also choose OpenCV with Python, OpenCV with C++, or MATLAB to learn and implement computer vision applications.

OpenCV with Python could be the most preferred choice for beginners due to its flexibility, simple syntax, and versatility. Various reasons make Python the best programming language for computer vision, which is as follows:

Easy-to-use: Python is very famous as it is easy to learn for entry-level persons and professionals. Further, Python is also easily adaptable and covers all business needs.

Most used programming language: Python is one of the most popular programming languages as it contains complete learning environments to get started with machine learning, artificial intelligence, deep learning, and computer vision.

Debugging and visualization: Python has an in-built facility for debugging via 'PDB' and visualization through Matplotlib.

Computer Vision Challenges

Computer vision has emerged as one of the most growing domains of artificial intelligence, but it still has a few challenges to becoming a leading technology. There are a few challenges observed while working with computer vision technology.

Reasoning and analytical issuesAll programming languages and technologies require the basic logic behind any task. To become a computer vision expert, you must have strong reasoning and analytical skills. If you don't have such skills, then defining any attribute in visual content may be a big problem.

Privacy and security: Privacy and security are among the most important factors for any country. Similarly, vision-powered surveillance is also having various serious privacy issues for lots of countries. It restricts users from accessing unauthorized content. Further, various countries also avoid such face recognition and detection techniques for privacy and security reasons.

Duplicate and false content: Cyber security is always a big concern for all organizations, and they always try to protect their data from hackers and cyber fraud. A data breach can lead to serious problems, such as creating duplicate images and videos over the internet.

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Computer Vision | Computer Vision Tutorial - javatpoint

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Computer Vision | Computer Vision Tutorial with How does Computer Vision Work, evolution of computer vision, Applications of computer vision, Challenges of computer vision etc.