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The image of the function is the set of all output values it may produce, that is, the image of . The preimage of f {\displaystyle f} , that is, the preimage of Y {\displaystyle Y} under f {\displaystyle f} , always equals X {\displaystyle X} (the domain of f {\displaystyle f} ); therefore, the former notion is rarely used.
The ideal case of epipolar geometry. A 3D point x is projected onto two camera images through lines (green) which intersect with each camera's focal point, O 1 and O 2. The resulting image points are y 1 and y 2. The green lines intersect at x. In practice, the image points y 1 and y 2 cannot be measured with arbitrary
The camera matrix derived in the previous section has a null space which is spanned by the vector = This is also the homogeneous representation of the 3D point which has coordinates (0,0,0), that is, the "camera center" (aka the entrance pupil; the position of the pinhole of a pinhole camera) is at O.
The image of a point source is also a three dimensional (3D) intensity distribution which can be represented by a 3D point-spread function. As an example, the figure on the right shows the 3D point-spread function in object space of a wide-field microscope (a) alongside that of a confocal microscope (c).
In computer vision, the fundamental matrix is a 3×3 matrix which relates corresponding points in stereo images.In epipolar geometry, with homogeneous image coordinates, x and x′, of corresponding points in a stereo image pair, Fx describes a line (an epipolar line) on which the corresponding point x′ on the other image must lie.
All points X e.g. X 1, X 2, X 3 on the O L –X L line will verify that constraint. It means that it is possible to test if two points correspond to the same 3D point. Epipolar constraints can also be described by the fundamental matrix , [ 1 ] or in the case of noramlized image coordatinates, the essential matrix [ 2 ] between the two cameras.
A point R at the intersection of the optical axis and the image plane. This point is referred to as the principal point [2] or image center. A point P somewhere in the world at coordinate (,,) relative to the axes X1, X2, and X3. The projection line of point P into the camera.
Consider 4 points on a circle in the original image (left). The circle Hough transform is shown in the right. Note that the radius is assumed to be known. For each (x,y) of the four points (white points) in the original image, it can define a circle in the Hough parameter space centered at (x, y) with radius r.