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Basics of Computer Vision

Princeton University_010722D
[Princeton University - Office of Communications]

 

- Overview

Computer vision 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 which aims to replicate human perception and associated brain functions to acquire, analyse, process, understand and thereafter work on an image. Computer vision 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 computer vision hardware and software algorithms deploying deep learning technologies are witnessing success in identifying objects

The initial goal of computer vision 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 Technologies

Computer Vision gives us the ability to teach a computer to make meaning of the physical world through vision. These tools allow us to develop applications that can make meaning from the input of cameras, photos, and videos to mind-bending degrees.

As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems. Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, object pose estimation, learning, indexing, and image restoration. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. 

In 1995 everyone in tech wanted a slice of the dot-com boom, but today, fields like artificial intelligence (AI), machine learning (ML) and big data drive the tech venture capital (VC) of the world to dig into their pockets. Computer vision is at the intersection of all these data-driven innovations. While uses for computer vision are well-known within the tech world, the term is still virtually unknown to the general public, even though many of them are already benefiting from it.

 

- Research of Computer Vision Technologies

There is a lot of research being done in the computer vision field, but it’s not just research. Real-world applications demonstrate how important computer vision is to endeavors in business, entertainment, transportation, healthcare and everyday life. A key driver for the growth of these applications is the flood of visual information flowing from smartphones, security systems, traffic cameras and other visually instrumented devices. This data could play a major role in operations across industries, but today goes unused. The information creates a test bed to train computer vision applications and a launchpad for them to become part of a range of human activities. 

Computer vision is used in industries ranging from energy and utilities to manufacturing and automotive. With Deep Learning (DL), a lot of new applications of computer vision technologies have been introduced. For example, we may use computer vision technologies to process medical images. These technologies help doctors detect malign changes such as tumors and hardening of the arteries and provide highly accurate measurements of organs and blood flow. 

Some medical startups claim they’ll soon be able to use computers to read X-rays, MRIs, and CT scans more rapidly and accurately than radiologists, to diagnose cancer earlier and less invasively, and to accelerate the search for life-saving pharmaceuticals. Hospitals and imaging centers that can interpret images faster and more accurately with the use of fewer radiologists. 

Business enterprises are developing computer vision systems embedded into deep learning systems hosted on the edge of the Internet of Things (IoT), in on-board systems, performing inference analysis in the cloud.

 


[More to come ...]

 

 

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