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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
Richard I. Hartley FAA is an Australian computer scientist and an Emeritus professor at the Australian National University, where he is a member of the Computer Vision group in the Research School of Computing.
Image registration has applications in remote sensing (cartography updating), and computer vision. Due to the vast range of applications to which image registration can be applied, it is impossible to develop a general method that is optimized for all uses.
Computer Vision. Prentice Hall. ISBN 0-13-030796-3. Gang Xu and Zhengyou Zhang (1996). Epipolar geometry in Stereo, Motion and Object Recognition. Kluwer Academic Publishers. ISBN 0-7923-4199-6. Szeliski, Richard (2022). Computer Vision: Algorithms and Applications (2 ed.). Springer Nature. p. 925. ISBN 3030343723
The Harris corner detector is a standard algorithm for corner detection in computer vision. [6] The algorithm works by analyzing the eigenvalues of the 2D discrete structure tensor matrix at each image pixel and flagging a pixel as a corner when the eigenvalues of its structure tensor are sufficiently large. Intuitively, the eigenvalues of the ...
In computer vision, the fundamental matrix 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.
A vision transformer (ViT) is a transformer designed for computer vision. [1] A ViT decomposes an input image into a series of patches (rather than text into tokens ), serializes each patch into a vector, and maps it to a smaller dimension with a single matrix multiplication .