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Computer Vision Research and Applications

[Computer Vision - Carnegie Mellon University]

- Overview

Computer vision (CV) is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand. 

One of the booms of emerging technologies is computer vision (CV) which aims to replicate human perception and associated brain functions to acquire, analyse, process, understand and thereafter work on an image. CV is a subdomain of Artificial Intelligence (AI) that deals with how computers gain high level understanding through acquiring, processing and analyzing digital images and video. 

Replicating this process is extremely challenging as designers find it hard to analyse what hardware and software is required to perform the exact match to a customer’s requirements and has the maximum probability of selection. After years of hard-work, businesses using CV hardware and software algorithms deploying deep learning technologies are witnessing success in identifying objects.


- The Technologies of Computer Vision

The initial goal of computer vision (CV) was to enable machines to see the visual world and interpret it the way a human would, but Artificial Intelligence (AI) has advanced computer vision beyond human vision and now machines can see things humans can’t, like air quality and temperature. Big data is essential to furthering what computer vision can recognize and the conclusions it draws from what it sees, which is why companies leading the way in the field are tech giants that already have a foot in the data gathering and machine learning door.

Computer vision uses convolutional neural networks (CNNs) to process visual data at the pixel level and deep learning recurrent neural networks (RNNs) to understand the relationship between one pixel and another.

Uses of computer vision include:

  • Biometric Access Management - CV plays an important role in facial and iris recognition.
  • Industrial Robotics and Autonomous Vehicles – CV allows robots and autonomous vehicles to avoid collisions and navigate safely.
  • Digital Diagnostics – CV can be used in conjunction with other types of AI programming to automate X-ray and MRI analysis.
  • Augmented Reality - CV allows mixed reality programming to know where virtual objects should be placed.


- The Rise of Computer Vision

To a computer, the image above - like all images - is an array of pixels representing numerical values ​​for shades of red, green, and blue. One of the challenges that computer scientists have been grappling with since the 1950s is creating machines that can understand photos and videos like humans do. The field of computer vision has become one of the hottest areas of computer science and artificial intelligence (AI) research. Decades later, we've come a long way in creating software that can understand and describe the content of visual data. But we also discovered how far we have to go to understand and replicate one of the fundamental functions of the human brain. 

Computer vision (CV) has exploded over the past few years, and it is now able to identify objects with astonishing accuracy, driving advances in everything from surveillance cameras to autonomous vehicles. There are two main reasons for the rapid development of computer vision. , which uses artificial intelligence to interpret and process the scene seen by cameras and other devices.

  • First, millions of images are now labeled thanks to the web, allowing robotic vision systems to train themselves how to recognize what's in a scene using a form of artificial intelligence called deep learning.
  • Second, a new generation of graphics processing units, or GPUs, originally developed for the video game industry, can learn and recognize images faster. Furthermore, the processing architectures used by deep networks mimic the human visual system, even to the point of assigning network layers so they reflect the arrangement of functional brain regions that humans use to view. "


- The Goal of Computer Vision

At an abstract level, the goal of computer vision problems is to infer the world using observed image data. It is a multidisciplinary field that can be broadly referred to as a subfield of artificial intelligence and machine learning that may involve the use of specialized methods and the use of general learning algorithms. 

Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects, and then react to what they "see". From recognizing faces to processing live performances of soccer matches, computer vision can match or even surpass human visual abilities in many areas. 

Since computer vision represents a relative understanding of the visual environment and its context, many scientists believe that the field paves the way for general artificial intelligence due to its cross-domain mastery. Computer vision is currently one of the hottest research areas in deep learning. It is located at the intersection of many disciplines such as computer science (graphics, algorithms, theory, systems, architecture), mathematics (information retrieval, machine learning), engineering (robotics, speech, NLP, image processing), physics (optics), Biology (Neuroscience) and Psychology (Cognitive Science).


Mount Fuji_Japan_062122A
[Mount Fuji, Japan]

- Application Domains of Computer Vision

Computer vision is an artificial intelligence (AI) technology through which robots can see. It plays a vital role in safety, security, health, access and entertainment. Computer vision automatically extracts, analyzes and understands useful information from a single image or a group of images. The process involves developing algorithms to enable automatic visual understanding. 

  • Computer vision has numerous applications including: agriculture, augmented reality, autonomous vehicles, biometrics, character recognition, forensics, industrial quality inspection, face recognition, gesture analysis, geosciences, image inpainting, Medical image analysis, contamination monitoring, process control, remote sensing, robotics, security and surveillance, transportation, and more.
  • Computer Vision: Fundamentals and Applications - What do the following technologies have in common: robots that can navigate space and perform tasks, search engines that can index billions of images and videos, and diagnostic tools that can diagnose medical images Algorithmic disease, or smart cars that can be seen and driven safely? At the heart of these modern AI applications are computer vision techniques that can perceive, understand and reconstruct the complex visual world. Computer vision is the fastest growing and most exciting Artificial intelligence is one of the disciplines in academia and industry today.
  • Facebook combines computer vision, machine learning, and their massive photo dataset to obtain highly accurate facial recognition results. Facebook has tons of photos from users. Many of them have been tagged, identifying the person in the photo. As photos are tagged, Facebook can run their computer vision algorithms on those photos. At a very high level and given enough data, the algorithm can learn to recognize a person's face from relevant tags on a photo. Not only that, but Facebook can also use the same process to identify objects in images.


- Face Recognition Technology and Privacy

The Facial Recognition Technology (FRT) is used to match a photo of a person's face through a database that contains picture, name, and other records of someone that are already in the database. This technology uses biometric data with other available information and provides precise and accurate information about a person and his behaviour. 

FRT has positioned itself significantly advanced among all biometric-based technologies. The use of FRT by government agencies and commercial organisation comes under scrutiny as many of them use the technology in violation of right to privacy where the data subjects are either not informed of data collection or not consented for the data collection, use or storage of their data. 

Privation of regulatory measures allows government agencies and commercial organisations to operate with no real legal restraint and only under limited self-regulation in many common law countries. 



[More to come ...] 

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