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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Fundamental matrix (computer vision) ： ウィキペディア英語版 Fundamental matrix (computer vision) In computer vision, the fundamental matrix $\backslash mathbf$ 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 : $\backslash mathbf\text{'}^\; \backslash mathbf\; =\; 0.$ 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 the fundamental matrix was published in 1992 by both Faugeras and Hartley. Although Longuet-Higgins' essential matrix satisfies a similar relationship, the essential matrix is a metric object pertaining to calibrated cameras, while the fundamental matrix describes the correspondence in more general and fundamental terms of projective geometry. This is captured mathematically by the relationship between a fundamental matrix $\backslash mathbf$ and its corresponding essential matrix $\backslash mathbf$, which is : $\backslash mathbf\; =\; \backslash mathbf\text{'}^\; \backslash ;\; \backslash mathbf\; \backslash ;\; \backslash mathbf$ $\backslash mathbf$ and $\backslash mathbf\text{'}$ being the intrinsic calibration matrices of the two images involved. == Introduction == The fundamental matrix is a relationship between any two images of the same scene that constrains where the projection of points from the scene can occur in both images. Given the projection of a scene point into one of the images the corresponding point in the other image is constrained to a line, helping the search, and allowing for the detection of wrong correspondences. The relation between corresponding image points which the fundamental matrix represents is referred to as ''epipolar constraint'', ''matching constraint'', ''discrete matching constraint'', or ''incidence relation''. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Fundamental matrix (computer vision)」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.