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A geotagged photograph is a photograph which is associated with a geographic position by geotagging. Usually this is done by assigning at least a latitude and longitude to the image, and optionally elevation, compass bearing and other fields may also be included. In theory, every part of a picture can be tied to a geographic location, but in ...
The collinearity equations are a set of two equations, used in photogrammetry and computer stereo vision, to relate coordinates in a sensor plane (in two dimensions) to object coordinates (in three dimensions). The equations originate from the central projection of a point of the object through the optical centre of the camera to the image on ...
Camera matrix. In computer vision a camera matrix or (camera) projection matrix is a matrix which describes the mapping of a pinhole camera from 3D points in the world to 2D points in an image. Let be a representation of a 3D point in homogeneous coordinates (a 4-dimensional vector), and let be a representation of the image of this point in the ...
Pinhole camera model. Model of 3D points projected onto planar image via a lens-less aperture. A diagram of a pinhole camera. The pinhole camera model describes the mathematical relationship between the coordinates of a point in three-dimensional space and its projection onto the image plane of an ideal pinhole camera, where the camera aperture ...
Geotagging. Geotagging, or GeoTagging, is the process of adding geographical identification metadata to various media such as a geotagged photograph or video, websites, SMS messages, QR Codes or RgSS feeds and is a form of geospatial metadata. This data usually consists of latitude and longitude coordinates, though they can also include ...
Triangulation (computer vision) In computer vision, triangulation refers to the process of determining a point in 3D space given its projections onto two, or more, images. In order to solve this problem it is necessary to know the parameters of the camera projection function from 3D to 2D for the cameras involved, in the simplest case ...
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The essential matrix is then defined as: {\displaystyle \mathbf {E} = [\mathbf {t} ]_ {\times }\mathbf {R} } where is the matrix representation of the cross product with . Note: Here, the transformation will transform points in the 2nd view to the 1st view. For the definition of we are only interested in the orientations of the normalized image ...