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of the object's pose. Once a set of control points on the object, typically corners or other feature points, has been identified, it is then possible to solve the pose transformation from a set of equations which relate the 3D coordinates of the points with their 2D image coordinates. Algorithms that determine the pose of a
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Learning-based methods: These methods use artificial learning-based system which learn the mapping from 2D image features to pose transformation. In short, this means that a sufficiently large set of images of the object, in different poses, must be presented to the system during a learning phase.
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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
64:. This information can then be used, for example, to allow a robot to manipulate an object or to avoid moving into the object based on its perceived position and orientation in the environment. Other applications include skeletal action recognition.
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or be restricted for the estimation of the intrinsic parameters only. Exterior orientation and interior orientation refer to the determination of only the extrinsic and intrinsic parameters, respectively.
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82:. Pose estimation problems can be solved in different ways depending on the image sensor configuration, and choice of methodology. Three classes of methodologies can be distinguished:
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may be used. This approach is robust especially when the images are not perfectly calibrated. In this particular case, the pose represent the
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Once the learning phase is completed, the system should be able to present an estimate of the object's pose given an image of the object.
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167:(position and orientation) while the intrinsic parameters specify the camera image format (focal length, pixel size, and image origin).
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where the camera projection matrices of two cameras are used to calculate the 3D world coordinates of a point viewed by both cameras.
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This process is often called geometric camera calibration or simply camera calibration, although that term may also refer to
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The specific task of determining the pose of an object in an image (or stereo images, image sequence) is referred to as
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Intelligent Robots and
Computer Vision XV: Algorithms, Techniques, Active Vision, and Materials Handling
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approximating the camera that produced a given photograph or video; it determines which incoming
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In computer vision, the pose of an object is often estimated from camera input by the process of
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The classic camera calibration requires special objects in the scene, which is not required in
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is associated with each pixel on the resulting image. Basically, the process determines the
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307:"Transformation matrices to geometry_msgs/Pose - ROS Answers: Open Source Q&A Forum"
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and the error between the projection of the object control points with the image is the
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Hoff, William A.; Nguyen, Khoi; Lyon, Torsten (1996-10-29). Casasent, David P. (ed.).
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methods: If the pose of an object does not have to be computed in real-time a
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Usually, the camera parameters are represented in a 3 × 4
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algorithms, if the correspondences between points are not already known.
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247:"Computer-vision-based registration techniques for augmented reality"
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Camera resectioning is often used in the application of
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47:. Poses are often stored internally as
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321:"Drake: Spatial Pose and Transform"
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172:photometric camera calibration
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216:Structure from motion
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211:Camera calibration
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