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Epipolar Constraint
Epipolar geometry is the geometry of stereo vision. When two cameras view a 3D scene from two distinct positions, there are a number of geometric relations between the 3D points and their projections onto the 2D images that lead to constraints between the image points. These relations are derived based on the assumption that the cameras can be approximated by the pinhole camera model. Definitions The figure below depicts two pinhole cameras looking at point X. In real cameras, the image plane is actually behind the focal center, and produces an image that is symmetric about the focal center of the lens. Here, however, the problem is simplified by placing a ''virtual image plane'' in front of the focal center i.e. optical center of each camera lens to produce an image not transformed by the symmetry. OL and OR represent the centers of symmetry of the two cameras lenses. X represents the point of interest in both cameras. Points xL and xR are the projections of point X onto th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
3D Scanner
3D scanning is the process of analyzing a real-world object or environment to collect data on its shape and possibly its appearance (e.g. color). The collected data can then be used to construct digital 3D modelling, 3D models. A 3D scanner can be based on many different technologies, each with its own limitations, advantages and costs. Many limitations in the kind of objects that can be digitization, digitised are still present. For example, optical technology may encounter many difficulties with dark, shiny, reflective or transparent objects. For example, industrial computed tomography scanning, structured-light 3D scanners, Lidar, LiDAR and Time-of-flight camera, Time Of Flight 3D Scanners can be used to construct digital 3D modeling, 3D models, without non-destructive testing, destructive testing. Collected 3D data is useful for a wide variety of applications. These devices are used extensively by the entertainment industry in the production of movies and video games, includ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Peter Giblin
Peter John Giblin (10 July 1943) is an English mathematician whose primary research involves singularity theory and its application to geometry, computer vision, and computer graphics. Giblin is an emeritus professor of mathematics at the University of Liverpool where he has served on staff for more than 40 years. His positions at Liverpool have included Head of Department (of mathematical sciences), and Head of Division (of pure mathematics). He is the author or co-author of eight published books, some of which have been translated into Russian. The foreword for the Russian translation of his book ''Curves and Singularities'' was written by V. I. Arnold. Giblin has also authored or co-authored over a hundred peer review Peer review is the evaluation of work by one or more people with similar competencies as the producers of the work (peers). It functions as a form of self-regulation by qualified members of a profession within the relevant field. Peer review ...ed publis ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Roberto Cipolla
Roberto Cipolla (born 1963), , FREng, is a British researcher in computer vision and Professor of Information Engineering at the University of Cambridge. Education Cipolla was born in Solihull, England and attended Langley School in Solihull and Solihull Sixth Form College. He studied engineering at the University of Cambridge and graduated in 1984 from Queens' College. He obtained an Master's (MSE) degree from the University of Pennsylvania in 1985 and then was a visiting researcher at the Electrotechnical Laboratory in Tsukuba, studied Japanese at the Osaka University of Foreign Studies, and gained a second master's degree (MEng) from the University of Electro-Communications in Tokyo. Cipolla returned to England in 1988 and studied at the University of Oxford (Balliol College). In 1991 he was awarded a D.Phil. (Computer Vision) for his work on 3D reconstruction from smooth 2D contours. Career From 1991 to 1992 Cipolla was a Toshiba Fellow and engineer at the Toshiba C ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linda Shapiro
Linda G. Shapiro is a professor in the Department of Computer Science and Engineering, a Professor of Electrical Engineering, and Adjunct Professor of Biomedical Informatics and Medical Education at the University of Washington. Education and Experience Shapiro graduated with a B.S. with highest distinction in Mathematics and Computer Science from the University of Illinois in 1970. She completed her M.S. in Computer Science from University of Iowa in 1972 and her Ph.D. in Computer Science from University of Iowa in 1974. She was a faculty member in Computer Science at Kansas State University from 1974 to 1978 and at Virginia Polytechnic Institute and State University from 1979 to 1984. She then spent two years as Director of Intelligent Systems at Machine Vision International in Ann Arbor, Michigan. She has been an IEEE Fellow since 1995, an IAPR fellow since 2000, and has been editor-in-chief of ''CVGIP: Image Understanding''. Professor Shapiro received the Pattern Recognition ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SRI International
SRI International (SRI) is an American nonprofit scientific research institute and organization headquartered in Menlo Park, California. The trustees of Stanford University established SRI in 1946 as a center of innovation to support economic development in the region. The organization was founded as the Stanford Research Institute. SRI formally separated from Stanford University in 1970 and became known as SRI International in 1977. SRI performs client-sponsored research and development for government agencies, commercial businesses, and private foundations. It also licenses its technologies, forms strategic partnerships, sells products, and creates spin-off companies. SRI's headquarters are located near the Stanford University campus. SRI's annual revenue in 2014 was approximately $540 million, which tripled from 1998 under the leadership of Curtis Carlson. In 1998, the organization was on the verge of bankruptcy when Carlson took over as CEO. Over the next sixteen years wit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence Center
The Artificial Intelligence Center is a laboratory in the Information and Computing Sciences Division of SRI International. It was founded in 1966 by Charles Rosen and studies artificial intelligence. One of their early projects was Shakey the Robot, the first general-purpose mobile robot. More recently, the center funded early development of CALO and Siri. The center has also provided the military with various technology. See also * Augmentation Research Center SRI International's Augmentation Research Center (ARC) was founded in the 1960s by electrical engineer Douglas Engelbart to develop and experiment with new tools and techniques for collaboration and information processing. The main product to come ... References External links SRI International Artificial intelligence laboratories {{compu-AI-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Richard Hartley (scientist)
Richard I. Hartley is an Australian computer scientist and a professor at the Australian National University, where he is head of the Computer Vision group in the Research School of Engineering. Biography In 1971, Hartley received a BSc degree from the Australian National University followed by MSc (1972) and PhD (1976) degrees in mathematics from the University of Toronto. He also obtained an MSc degree in computer science from Stanford University in 1983. Scientific work His work is primarily devoted to the fields of Artificial intelligence, Image processing, and Computer vision. He is best known for his 2000 book ''Multiple View Geometry in computer vision'', written with Andrew Zisserman, now in its second edition (2004). According to WorldCat, the book is held in 1428 libraries Publications Hartley has published a wide variety of articles in computer science on the topics of computer vision and optimization. The following are his most highly cited works * 2000 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Trifocal Tensor
In computer vision, the trifocal tensor (also tritensor) is a 3×3×3 array of numbers (i.e., a tensor) that incorporates all projective geometric relationships among three views. It relates the coordinates of corresponding points or lines in three views, being independent of the scene structure and depending only on the relative motion (i.e., pose) among the three views and their intrinsic calibration parameters. Hence, the trifocal tensor can be considered as the generalization of the fundamental matrix in three views. It is noted that despite the tensor being made up of 27 elements, only 18 of them are actually independent. There is also a so-called calibrated trifocal tensor, which relates the coordinates of points and lines in three views given their intrinsic parameters and encodes the relative pose of the cameras up to global scale, totalling 11 independent elements or degrees of freedom. The reduced degrees of freedom allow for fewer correspondences to fit the model, at ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fundamental Matrix (computer Vision)
In computer vision, the fundamental matrix \mathbf is a 3×3 matrix which relates corresponding points in stereo images. In epipolar geometry, with homogeneous image coordinates, x and x′, of corresponding points in a stereo image pair, Fx describes a line (an epipolar line) on which the corresponding point x′ on the other image must lie. That means, for all pairs of corresponding points holds : \mathbf'^ \mathbf = 0. Being of rank two and determined only up to scale, the fundamental matrix can be estimated given at least seven point correspondences. Its seven parameters represent the only geometric information about cameras that can be obtained through point correspondences alone. The term "fundamental matrix" was coined by QT Luong in his influential PhD thesis. It is sometimes also referred to as the "bifocal tensor". As a tensor it is a two-point tensor in that it is a bilinear form relating points in distinct coordinate systems. The above relation which defines ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Essential Matrix
In computer vision, the essential matrix is a 3 \times 3 matrix, \mathbf that relates corresponding points in stereo images assuming that the cameras satisfy the pinhole camera model. Function More specifically, if \mathbf and \mathbf' are homogeneous ''normalized'' image coordinates in image 1 and 2, respectively, then : (\mathbf')^\top \, \mathbf \, \mathbf = 0 if \mathbf and \mathbf' correspond to the same 3D point in the scene. The above relation which defines the essential matrix was published in 1981 by H. Christopher Longuet-Higgins, introducing the concept to the computer vision community. Richard Hartley and Andrew Zisserman's book reports that an analogous matrix appeared in photogrammetry long before that. Longuet-Higgins' paper includes an algorithm for estimating \mathbf from a set of corresponding normalized image coordinates as well as an algorithm for determining the relative position and orientation of the two cameras given that \mathbf is kno ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Photogrammetry
Photogrammetry is the science and technology of obtaining reliable information about physical objects and the environment through the process of recording, measuring and interpreting photographic images and patterns of electromagnetic radiant imagery and other phenomena. The term photogrammetry was coined by the Prussian architect Albrecht Meydenbauer, which appeared in his 1867 article "Die Photometrographie." There are many variants of photogrammetry. One example is the extraction of three-dimensional measurements from two-dimensional data (i.e. images); for example, the distance between two points that lie on a plane parallel to the photographic image plane can be determined by measuring their distance on the image, if the scale (map), scale of the image is known. Another is the extraction of accurate color ranges and values representing such quantities as albedo, specular reflection, Metallicity#Photometric colors, metallicity, or ambient occlusion from photographs of mater ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
