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Computer Vision

Bern_Switzerland_DSC_0779
(Bern, Switzerland - Alvin Wei-Cheng Wong)
 

 

As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery.

Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, 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).

 

The Rise of Computer Vision 

 

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.

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.

 

Computer Vision - Technology That Sees The World

 
Robot Vision's Family Tree
(Robot Vision's Family Tree - ROBOTIQ)
Machine Perception gives a machine the ability to explain, in a human manner, why it is making its decisions, to warn when it is about to fail, and to provide an understandable characterization of its failures. 
 
Computer Vision builds machines that can see the world like humans do, and involves designing algorithms that can answer questions about a photograph or a video.
 
  • The goal of computer vision is to make computers see the world around them. Vision algorithms are starting to impact our everyday lives more and more. They help to keep cars safely on the road, enable remote robotics operations in hazardous environments, reconstruct 3D models of cities, and organize your personal photos.
  • Computer vision systems can understand images and video, for example, building extensive geometric and physical models of cities from video, or warning construction workers about nearby dangers. 
  • One of the most complex and high profile ways computer vision is being used is in the advancement of autonomous cars. Driverless vehicles depend on advanced AI computer vision, with deep machine learning woven throughout for guidance. Since data is the gas that fuels AI and ML advancement, it’s no surprise that Google owns the most advanced driverless technology on the market.
  • [The Ohio State University]: The goal of computer vision is to make useful decisions about real physical objects and scenes based on sensed images and video. It is the process of discovering from images “what" is present in the world, “where" it is, and “what" it is doing, with the overall aim of constructing scene descriptions from the imagery. Algorithms require representations of shape, motion, color, context, etc. to perform the task.
  • [The British Machine Vision Association (BMVA)]: "Humans use their eyes and their brains to see and visually sense the world around them. Computer vision is the science that aims to give a similar, if not better, capability to a machine or computer. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding." 
  • [SUNY-Buffalo]: "Computer vision is an interdisciplinary field drawing on concepts from signal processing, artificial intelligence, neurophysiology, and perceptual psychology. The primary goal of computer vision research is to endow artificial systems with the capacity to see and understand visual imagery at a level rivaling or exceeding human vision."

 

 

[More to come ...] 

 
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