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  2. Image rectification - Wikipedia

    en.wikipedia.org/wiki/Image_rectification

    If the images to be rectified are taken from camera pairs without geometric distortion, this calculation can easily be made with a linear transformation.X & Y rotation puts the images on the same plane, scaling makes the image frames be the same size and Z rotation & skew adjustments make the image pixel rows directly line up [citation needed].

  3. OpenCV - Wikipedia

    en.wikipedia.org/wiki/OpenCV

    The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008. The second major release of the OpenCV was in October 2009.

  4. Image registration - Wikipedia

    en.wikipedia.org/wiki/Image_registration

    For low SNR data, the best image registration is achieved by cross-correlating all permutations of images in an image stack. [22] Image registration is an essential part of panoramic image creation. There are many different techniques that can be implemented in real time and run on embedded devices like cameras and camera-phones.

  5. Homography (computer vision) - Wikipedia

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

    This has many practical applications, such as image rectification, image registration, or camera motion—rotation and translation—between two images. Once camera resectioning has been done from an estimated homography matrix, this information may be used for navigation, or to insert models of 3D objects into an image or video, so that they ...

  6. Computer stereo vision - Wikipedia

    en.wikipedia.org/wiki/Computer_stereo_vision

    Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera.By comparing information about a scene from two vantage points, 3D information can be extracted by examining the relative positions of objects in the two panels.

  7. 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.

  8. Chessboard detection - Wikipedia

    en.wikipedia.org/wiki/Chessboard_detection

    The main limitation of using chessboard patterns for geometric camera calibration is that due to their highly repetitive structure, they need to be completely visible in the camera image. This assumption may be violated e.g. when specular reflections due to inhomogenous lighting cause chessboard detection to fail in some of the corners.

  9. Computer vision - Wikipedia

    en.wikipedia.org/wiki/Computer_vision

    Image acquisition – A digital image is produced by one or several image sensors, which, besides various types of light-sensitive cameras, include range sensors, tomography devices, radar, ultra-sonic cameras, etc. Depending on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence.