On Computing The Perspective Transformation Matrix and Camera Parameters
Author(s) -
Tieniu Tan,
G. D. Sullivan,
K. D. Baker
Publication year - 1993
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.7.13
Subject(s) - robustness (evolution) , perspective (graphical) , computer science , computation , transformation matrix , essential matrix , camera resectioning , transformation (genetics) , algorithm , matrix (chemical analysis) , artificial intelligence , computer vision , symmetric matrix , state transition matrix , biochemistry , chemistry , physics , eigenvalues and eigenvectors , materials science , kinematics , classical mechanics , quantum mechanics , composite material , gene
Camera calibration often entails the computation of the perspective transformation matrix. Conventionally, the matrix has been calculated by the standard linear least squares technique. Recently, Faugeras and Toscani have criticised the conventional approach for producing unsatisfactory, even "absurd", solutions, and have proposed an alternative approach. It is shown in this paper that their criticism of the conventional approach is misplaced and misleading. Experimental results demonstrate that Faugeras and Toscani's approach has no advantage over the conventional approach from the practical point of view. In fact, the latter is shown to be superior both in noise robustness and in computational cost. The paper also reports a method to resolve the possible sign ambiguities in the camera parameters computed by existing algorithms.
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