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Computer vision is an interdisciplinary field that deals with how computers can be made to gain 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.
Computing Rectifying Homographies for Stereo Vision by Charles Loop and Zhengyou Zhang (April 8, 1999) Microsoft Research; Computer Vision: Algorithms and Applications, Section 11.1.1 "Rectification" by Richard Szeliski (September 3, 2010) Springerdheerajnkumar
Foundations and Trends in Computer Graphics and Vision is a journal published by Now Publishers. It publishes survey and tutorial articles on all aspects of computer graphics and vision . [ 1 ] The editor-in-chiefs are Brian Curless ( University of Washington ), Luc Van Gool ( KU Leuven ) and Richard Szeliski ( Microsoft Research ).
Richard Szeliski, Image Alignment and Stitching: A Tutorial. Foundations and Trends in Computer Graphics and Computer Vision, 2:1-104, 2006. B. Fischer, J. Modersitzki: Ill-posed medicine – an introduction to image registration. Inverse Problems, 24:1–19, 2008; Barbara Zitová, Jan Flusser: Image registration methods: a survey. Image Vision ...
Rick Szeliski (2010), Computer Vision: Algorithms and Applications, Springer. Computational Photography: Methods and Applications (Ed. Rastislav Lukac), CRC Press, 2010. Intelligent Image Processing (John Wiley and Sons book information). Comparametric Equations. GJB-1: Increasing the dynamic range of a digital camera by using the Wyckoff principle
The following is a non-complete list of applications which are studied in computer vision. In this category, the term application should be interpreted as a high level function which solves a problem at a higher level of complexity. Typically, the various technical problems related to an application can be solved and implemented in different ways.
The Caltech 101 data set was used to train and test several computer vision recognition and classification algorithms. The first paper to use Caltech 101 was an incremental Bayesian approach to one-shot learning, [ 4 ] an attempt to classify an object using only a few examples, by building on prior knowledge of other classes.
Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. [1] Application domains include cluster analysis in computer vision and image processing .