Full Text:
<2812>
CLC number: TP273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 0
Clicked: 5674
Developing rigid constraint for the estimation of pose and structure from a single image
| |||||||||||||
WEI Bao-gang, LIU Yong-huai. Developing rigid constraint for the estimation of pose and structure from a single image[J]. Journal of Zhejiang University Science A, 2004, 5(7): 773-781.
@article{title="Developing rigid constraint for the estimation of pose and structure from a single image",
author="WEI Bao-gang, LIU Yong-huai",
journal="Journal of Zhejiang University Science A",
volume="5",
number="7",
pages="773-781",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.0773"
}
%0 Journal Article
%T Developing rigid constraint for the estimation of pose and structure from a single image
%A WEI Bao-gang
%A LIU Yong-huai
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 7
%P 773-781
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.0773
TY - JOUR
T1 - Developing rigid constraint for the estimation of pose and structure from a single image
A1 - WEI Bao-gang
A1 - LIU Yong-huai
J0 - Journal of Zhejiang University Science A
VL - 5
IS - 7
SP - 773
EP - 781
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2004.0773
Abstract: Pose and structure estimation from a single image is a fundamental problem in machine vision and multiple sensor fusion and integration. In this paper we propose using rigid constraints described in different coordinate frames to iteratively estimate structural and camera pose parameters. Using geometric properties of reflected correspondences we put forward a new concept, the reflected pole of a rigid transformation. The reflected pole represents a general analysis of transformations that can be applied to both 2D and 3D transformations. We demonstrate how the concept is applied to calibration by proposing an iterative method to estimate the structural parameters of objects. The method is based on a coarse-to-fine strategy in which initial estimation is obtained through a classical linear algorithm which is then refined by iteration. For a comparative study of performance, we also implemented an extended motion estimation algorithm (from 2D-2D to 3D-2D case) based on epipolar geometry.
[1] Arun, K.S., Huang, T.S., Blostein, S.D., 1987. Least-squares fitting of two 3-D point sets.IEEE Trans. PAMI,9:698-700.
[2] Ball, R.S., 1900. A Treatise on the Theory of Screws. Cambridge University Press.
[3] Carceroni, R.L., Brown, C.M., 1997. Numerical Methods for Model-based Pose Recovery. Technical Report 659, Computer Science Department, The University of Rochester, Rochester, New York.
[4] Chaudhuri, S., Chatterjee, S., 1991. Performance analysis of total least squares methods in three-dimensional motion estimation.IEEE Trans. Robotics and Automation,7:707-714.
[5] Dornaika, F., Garcia, C., 1999. Pose estimation using point and line correspondences.Real Time Imaging,5:215-230.
[6] Eggert, D.W., Lorusso, A., Fisher R.B., 1997. Estimating 3-D rigid body transformations: a comparison of four major algorithms.MVA,9:272-290.
[7] Faugeras, O.D., Hebert, M., 1986. The representation, recognition, and locating of 3-D objects.Int. J. of Robotics Research,5:27-52.
[8] Haralick, R.M., Joo, H., Lee, C.N., Zhuang, X., Vaidya V.G., Kim, M.A., 1989. Pose estimation from corresponding point data.IEEE Trans. PAMI,9:1426-1441.
[9] Hattori, K., Matsumori, S., Sato, Y., 1998. Estimating Pose of Human Face Based on Symmetry Plane Using Range and Intensity Images. Proc. of Int. Conf. Pattern Recognition, p.1183-1187.
[10] Huang, T.S., Netravali, A.N., 1994. Motion and structure from feature correspondence: a review.Proceedings of the IEEE,82:252-268.
[11] Linnainmaa, S., Harwood, D., Davis, L.S., 1987. Pose determination of a three-dimensional object using triangular pairs.IEEE Trans. PAMI,10:634-647.
[12] Liu, Y., Rodrigues, M.A., 1999. Geometric Understanding of Rigid Body Transformations. Proc. IEEE Int. Conf. on Robotics and Automation, p.1275-1280.
[13] Lowe, D.G., 1991. Fitting parameterized three-dimensional models to images.IEEE Trans. PAMI,13:441-450.
[14] Matei, B., Meer, P., Tyler, D., 1998. Performance Assessment by Resampling: Rigid Motion Estimators. IEEE CS Workshop on Empirical Evaluation of Computer Vision Algorithms, Santa Barbara, California, p.72-95.
[15] McCarthy, J.M., 1990. Introduction to Theoretic Kinematics. The MIT Press.
[16] Mitiche, A., Aggarwal, J.K., 1986. A Computational Analysis of Time-varying Images.In: T.Y. Yuong, K.S. Fu, eds., Handbook of Pattern Recognition and Image Processing, Academic Press.
[17] Quan, L., Lan, Z., 1999. Linear N-point camera pose determination.IEEE Trans. PAMI,21:774-780.
[18] Rodrigues M.A., Liu, Y., 1999. A Comparative Study of Some 3D-2D Computer Vision Algorithms. Proc. the ISCA 14th Int. Conf. on Computers and Their Applications (CATA99), Cancun, Maxico, p.170-173.
[19] Toyama, F., Shoji, K., Miyamichi, J., 1998. Model-based Pose Estimation Using Genetic Algorithm. Proc. of Int. Conf. Pattern Recognition, p.198-201.
[20] Tsai, R.Y., Huang, T.S., 1984. Uniqueness and estimation of three-dimensional motion parameters of rigid objects with curved surfaces.IEEE Trans. PAMI,6:13-27.
[21] Umeyama, S., 1991. Least-squares estimation of transformation parameters between two point pattern.IEEE Trans. PAMI,13:377-380.
[22] Yamane, S., Izumi, M., Fukunaga, K., 1996. A method of model based pose estimation.Trans. IEICE D-II, J79-D-II,2:165-173.
Open peer comments: Debate/Discuss/Question/Opinion
<1>