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  2. Image rectification - Wikipedia

    en.wikipedia.org/wiki/Image_rectification

    Image rectification in GIS converts images to a standard map coordinate system. This is done by matching ground control points (GCP) in the mapping system to points in the image. These GCPs calculate necessary image transforms. [11] Primary difficulties in the process occur when the accuracy of the map points are not well known

  3. Triangulation (computer vision) - Wikipedia

    en.wikipedia.org/wiki/Triangulation_(computer...

    The problem to be solved there is how to compute (,,) given corresponding normalized image coordinates (,) and (′, ′). If the essential matrix is known and the corresponding rotation and translation transformations have been determined, this algorithm (described in Longuet-Higgins' paper) provides a solution.

  4. Camera matrix - Wikipedia

    en.wikipedia.org/wiki/Camera_matrix

    This type of camera matrix is referred to as a normalized camera matrix, it assumes focal length = 1 and that image coordinates are measured in a coordinate system where the origin is located at the intersection between axis X3 and the image plane and has the same units as the 3D coordinate system. The resulting image coordinates are referred ...

  5. Georeferencing - Wikipedia

    en.wikipedia.org/wiki/Georeferencing

    Graphical view of the affine transformation. The registration of an image to a geographic space is essentially the transformation from an input coordinate system (the inherent coordinates of pixels in the images based on row and column number) to an output coordinate system, a spatial reference system of the user's choice, such as the geographic coordinate system or a particular Universal ...

  6. Feature (computer vision) - Wikipedia

    en.wikipedia.org/wiki/Feature_(computer_vision)

    The result is often represented in terms of sets of (connected or unconnected) coordinates of the image points where features have been detected, sometimes with subpixel accuracy. When feature extraction is done without local decision making, the result is often referred to as a feature image. Consequently, a feature image can be seen as an ...

  7. Pose (computer vision) - Wikipedia

    en.wikipedia.org/wiki/Pose_(computer_vision)

    Analytic or geometric methods: Given that the image sensor (camera) is calibrated and the mapping from 3D points in the scene and 2D points in the image is known. If also the geometry of the object is known, it means that the projected image of the object on the camera image is a well-known function of the object's pose.

  8. Homography (computer vision) - Wikipedia

    en.wikipedia.org/wiki/Homography_(computer_vision)

    where and are the z coordinates of P in each camera frame and where the homography matrix is given by H a b = R − t n T d {\displaystyle H_{ab}=R-{\frac {tn^{T}}{d}}} . R {\displaystyle R} is the rotation matrix by which b is rotated in relation to a ; t is the translation vector from a to b ; n and d are the normal vector of the plane and ...

  9. Perspective-n-Point - Wikipedia

    en.wikipedia.org/wiki/Perspective-n-Point

    Perspective-n-Point [1] is the problem of estimating the pose of a calibrated camera given a set of n 3D points in the world and their corresponding 2D projections in the image. The camera pose consists of 6 degrees-of-freedom (DOF) which are made up of the rotation (roll, pitch, and yaw) and 3D translation of the camera with respect to the world.