KEYWORDS: Error analysis, Cameras, 3D metrology, 3D vision, 3D modeling, Mathematical modeling, Imaging systems, Visual process modeling, Stereo vision systems, Detection and tracking algorithms
Location measurement of 3D point in stereo vision is subjected to different sources of uncertainty that propagate to the final result. For current methods of error analysis, most of them are based on ideal intersection model to calculate the uncertainty region of point location via intersecting two fields of view of pixel that may produce loose bounds. Besides, only a few of sources of error such as pixel error or camera position are taken into account in the process of analysis. In this paper we present a straightforward and available method to estimate the location error that is taken most of source of error into account. We summed up and simplified all the input errors to five parameters by rotation transformation. Then we use the fast algorithm of midpoint method to deduce the mathematical relationships between target point and the parameters. Thus, the expectations and covariance matrix of 3D point location would be obtained, which can constitute the uncertainty region of point location. Afterwards, we turned back to the error propagation of the primitive input errors in the stereo system and throughout the whole analysis process from primitive input errors to localization error. Our method has the same level of computational complexity as the state-of-the-art method. Finally, extensive experiments are performed to verify the performance of our methods.
Image rectification reduce the search space from 2-dimension to 1-dimension and improve the searching efficiency of
stereo matching algorithm greatly. In this paper, a simple and convenient method, which fully considered image
sequence of monocular motion vision, is proposed to rectify the calibrated image sequence. The method is based on
coordinate system transformation, which can avoid the mass and complex computations, and the method rectifies image
sequence (three images) at once, which is efficient in image sequence processing. In this method, the rectification is
composed of several steps. Firstly, we establish a reference coordinate system by three movement position. The Z axis of
the reference coordinate system o_XYZ is the normal vector of the plane which three positions located. The direction of
X axis coincides with the baseline from position 2 to position 1. We set Y axis according to right-hand principle.
Secondly, we set the x axis and z axis of reference image space coordinate system o_xyz coincides with the X axis and Z
axis of the reference coordinate system, and the y axis is set to coincide with the line from position 2 to position 3.
Finally, we deduce a homography matrix to realize the image rectification. Both image data and computer simulation
data show that the method is an effective rectification method.
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