# computer vision technology

Computer vision is a branch of artificial intelligence. The technology helps to automate visual understanding from a sequence of images, videos, PDFs, or text images with the help of AI and Machine Learning (ML) algorithms.

Computer Vision, often abbreviated as CV, is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos.

The problem of computer vision appears simple because it is trivially solved by people, even very young children. Nevertheless, it largely remains an unsolved problem based both on the limited understanding of biological vision and because of the complexity of vision perception in a dynamic and nearly infinitely varying physical world.

Level set methods: Evolving interfaces in geometry, fluid mechanics, computer vision , and materials science

LEVEL SET METHODS AND FAST MARCHING METHODS. Level Set Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision , and Materials Science Cambridge Monographs on . Level Set methods University of California, Berkeley Evolution

Introductory techniques for 3-D computer vision

Introduction. images over time. More precisely, We are

A polyhedron representation for computer vision

My approach to computer vision is best characterized as inverse computer graphics. In computer graphics, the world is represented in sufficient detail so that the image forming process can be numerically simulated to generate synthetic television images; in the

Elastica and computer vision

the problem from differential geometry of describing those plane curves C which minimize the integral ∫ ( k^ 2+ β) ds. Here and β are constants, k is the curvature of C, ds the arc length and, to make the fewest boundary conditions, we mean minimizing for

Computer vision and fuzzy-neural systems

in neurai networks and fuzzy logic are trans-.. ofcomputer vision making it possible for applications to learn and make decisios, and visual data far more effectively. Now, Dr. Árun s together the latest research and applications. elds first comprehensive tutorial and

Statistical models of appearance for computer vision

The majority of tasks to which machine vision might usefully be applied arehard. The examples we use in this work are from medical image interpretation and face recognition, though the same considerations apply to many other domains. The most obvious reason for ABSTRACTA clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed. The MVE estimator identifies the least volume region containing h percent of the data points. The clustering algorithm iteratively partitions the

Real-time computer vision with OpenCV

In the past, an easy way to increase the performance of a computing device was to wait for the semiconductor processes to improve, which resulted in an increase in the devices clock speed. When the speed increased, all applications got faster without having to modify them

Concise computer vision

Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for un- dergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh

A robust competitive clustering algorithm with applications in computer vision

ABSTRACTb> Abstract /b> This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers. The proposed

Face recognition by humans: Nineteen results all computer vision researchers should know about

A key goal of computer vision researchers is to create automated face recognition systems that can equal, and eventually surpass, human performance. To this end, it is imperative that computational researchers know of the key findings from experimental studies of face

CAGD-based computer vision

ABSTRACTThe authors explore the connection between CAGD ( computer -aided geometric design) and computer vision . A method for the automatic generation of recognition strategies based on the 3-D geometric properties of shape has been devised

Markov random field image models and their applications to computer vision

Introduction. Computer vision refers to a variety of applications involving a sensing device, a computer , and software for restoring and possibly interpreting the sensed data. Most commonly, visible light is sensed by a video camera and converted to an array of measured Repeated visual inspection tasks, whether industrial or academic, are often tedious and labour intensive but nevertheless require specialist skill and careful judgement. There have- been many attempts to automate such processes. However, few have yet made the

Kinect depth sensor evaluation for computer vision applications

This technical report describes our evaluation of the Kinect depth sensor by Microsoft for Computer Vision applications. The depth sensor is able to return images like an ordinary camera, but instead of color, each pixel value represents the distance to the point. As suchIn the last few years a number of innovative systems have demonstrated the use of visual input and sophisticated computer processing to achieve a variety of manufacturing goals. Examples include automated visual inspection of transistor ignition as- semblies, printed

Robust techniques for computer vision

Visual information makes up about seventy five percent of all the sensorial information received by a person during a lifetime. This information is processed not only efficiently but also transparently. Our awe of visual perception was perhaps the best captured by the

Efficient graph-based energy minimization methods in computer vision