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  2. Triangulation (computer vision) - Wikipedia

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

    where ′, ′ are the homogeneous coordinates of the detected image points and , are the camera matrices. x (3D point) is the homogeneous representation of the resulting 3D point. The ∼ {\displaystyle \sim \,} sign implies that τ {\displaystyle \tau \,} is only required to produce a vector which is equal to x up to a multiplication by a non ...

  3. 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

  4. Bundle adjustment - Wikipedia

    en.wikipedia.org/wiki/Bundle_adjustment

    where (,) is the predicted projection of point on image and (,) denotes the Euclidean distance between the image points represented by vectors and . Because the minimum is computed over many points and many images, bundle adjustment is by definition tolerant to missing image projections, and if the distance metric is chosen reasonably (e.g ...

  5. 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 ...

  6. Diffusion map - Wikipedia

    en.wikipedia.org/wiki/Diffusion_map

    Diffusion maps exploit the relationship between heat diffusion and random walk Markov chain.The basic observation is that if we take a random walk on the data, walking to a nearby data-point is more likely than walking to another that is far away.

  7. 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 ...

  8. 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 ...

  9. 3D reconstruction from multiple images - Wikipedia

    en.wikipedia.org/wiki/3D_Reconstruction_from...

    Given a group of 3D points viewed by N cameras with matrices {} = …, define to be the homogeneous coordinates of the projection of the point onto the camera. The reconstruction problem can be changed to: given the group of pixel coordinates {}, find the corresponding set of camera matrices {} and the scene structure {} such that