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A New Generalized Computational Framework for Finding Object Orientation Using Perspective Trihedral Angle Constraint
October 1994 (vol. 16 no. 10)
pp. 961-975

This paper investigates a fundamental problem of determining the position and orientation of a three-dimensional (3-D) object using a single perspective image view. The technique is focused on the interpretation of trihedral angle constraint information. A new closed form solution based on Kanatani's formulation is proposed. The main distinguishing feature of the authors' method over the original Kanatani formulation is that their approach gives an effective closed form solution for a general trihedral angle constraint. The method also provides a general analytic technique for dealing with a class of problem of shape from inverse perspective projection by using "angle to angle correspondence information." A detailed implementation of the authors' technique is presented. Different trihedral angle configurations were generated using synthetic data for testing the authors' approach of finding object orientation by angle to angle constraint. The authors performed simulation experiments by adding some noise to the synthetic data for evaluating the effectiveness of their method in a real situation. It has been found that the authors' method worked effectively in a noisy environment which confirms that the method is robust in practical application.

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Index Terms:
computer vision; generalized computational framework; object orientation; perspective trihedral angle constraint; three-dimensional 3D object; single perspective image view; Kanatani's formulation; closed form solution; general analytic technique; shape from inverse perspective projection; angle to angle correspondence information; simulation experiments; noise; synthetic data; noisy environment
Citation:
Y. Wu, S.S. Iyengar, R. Jain, S. Bose, "A New Generalized Computational Framework for Finding Object Orientation Using Perspective Trihedral Angle Constraint," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 10, pp. 961-975, Oct. 1994, doi:10.1109/34.329012
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