AI News, BOOK REVIEW: Computer vision

Computer vision

Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos.

From the perspective of engineering, it seeks to automate tasks that the human visual system can do.[1][2][3] 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.[4][5][6][7] 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.[8] As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images.

From the perspective of engineering, it seeks to automate tasks that the human visual system can do.[1][2][3] '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.'[9] 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.[10] As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems.

It was meant to mimic the human visual system, as a stepping stone to endowing robots with intelligent behavior.[11] In 1966, it was believed that this could be achieved through a summer project, by attaching a camera to a computer and having it 'describe what it saw'.[12][13] What distinguished computer vision from the prevalent field of digital image processing at that time was a desire to extract three-dimensional structure from images with the goal of achieving full scene understanding.

Studies in the 1970s formed the early foundations for many of the computer vision algorithms that exist today, including extraction of edges from images, labeling of lines, non-polyhedral and polyhedral modeling, representation of objects as interconnections of smaller structures, optical flow, and motion estimation.[11] The next decade saw studies based on more rigorous mathematical analysis and quantitative aspects of computer vision.

This included image-based rendering, image morphing, view interpolation, panoramic image stitching and early light-field rendering.[11] Recent work has seen the resurgence of feature-based methods, used in conjunction with machine learning techniques and complex optimization frameworks.[15][16] Areas of artificial intelligence deal with autonomous planning or deliberation for robotical systems to navigate through an environment.

Some strands of computer vision research are closely related to the study of biological vision – indeed, just as many strands of AI research are closely tied with research into human consciousness, and the use of stored knowledge to interpret, integrate and utilize visual information.

On the other hand, it appears to be necessary for research groups, scientific journals, conferences and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented.

Modern military concepts, such as 'battlefield awareness', imply that various sensors, including image sensors, provide a rich set of information about a combat scene which can be used to support strategic decisions.

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.[4][5][6][7] 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.[8] The classical problem in computer vision, image processing, and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity.

Several specialized tasks based on recognition exist, such as: Several tasks relate to motion estimation where an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene, or even of the camera that produces the images .

By first analysing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches.

Some systems are stand-alone applications which solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc.

While inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing.

Inference and control requirements for IUS are: search and hypothesis activation, matching and hypothesis testing, generation and use of expectations, change and focus of attention, certainty and strength of belief, inference and goal satisfaction.[25] There are many kinds of computer vision systems, nevertheless all of them contain these basic elements: a power source, at least one image acquisition device (i.e.

While traditional broadcast and consumer video systems operate at a rate of 30 frames per second, advances in digital signal processing and consumer graphics hardware has made high-speed image acquisition, processing, and display possible for real-time systems on the order of hundreds to thousands of frames per second.

When combined with a high-speed projector, fast image acquisition allows 3D measurement and feature tracking to be realised.[26] Egocentric vision systems are composed of a wearable camera that automatically take pictures from a first-person perspective.

Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities.

Digital image processing

Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s at the Jet Propulsion Laboratory, Massachusetts Institute of Technology, Bell Laboratories, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement.[1] The cost of processing was fairly high, however, with the computing equipment of that era.

Affine transformations enable basic image transformations including scale, rotate, translate, mirror and sheer as is shown in the following examples show:[4] Digital cameras generally include specialized digital image processing hardware – either dedicated chips or added circuitry on other chips – to convert the raw data from their image sensor into a color-corrected image in a standard image file format Westworld (1973) was the first feature film to use the digital image processing to pixellate photography to simulate an android's point of view.[5]

Research

Computer image analysis largely contains the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital geometry, and signal processing.

On the other hand, the human visual cortex is an excellent image analysis apparatus, especially for extracting higher-level information, and for many applications - including medicine, security, and remote sensing - human analysts still cannot be replaced by computers.

Since perception can be seen as the extraction of information from sensory signals, computer vision can be seen as the scientific investigation of artificial systems for perception from images or multi-dimensional data.

Department of Electrical and Computer Engineering

Their focus is to reveal the algorithms of the brain through the discriminatory analysis of Big Data – a modern and popular keyword for the processing and understanding of vast amounts of information.

ECE Computer Vision courses explore the methods for acquiring, processing, analyzing and understanding images and high-dimensional data from the real world, in order to produce numerical or symbolic information.

ECE image processing courses show students how to apply mathematical operations to images in order to enhance understanding of their relatable characteristics or parameters.

Graduates have landed in the movie industry doing vision and graphics for Disney, found roles at Google, Apple or SnapChat, joined communication roles at Intel or IBM, or taken up software roles at Amazon. 

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