Some Properties of the E Matrix in Two-View Motion Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 11, Issue 12, 1989, Pages 1310-1312

  Huang, T S ^a   Faugeras, O D ^b  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL TECHNIQUES--MATRIX ALGEBRA;

E MATRIX; ESSENTIAL PARAMETERS; IMAGE MOTION ANALYSIS; MATRIX DECOMPOSITION/FACTORIZATION; ROTATION MATRIX; SKEW-SYMMETRICAL MATRIX;

IMAGE PROCESSING;

EID: 0024879059     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.41368     Document Type: Article

References (10)

1. H.C. Longuet-Higgins, “A computer program for reconstructing a scene from two projections,” Nature, vol. 392, pp. 133–135, 1981.
2. R.Y. Tsai and T.S. Huang, “Uniqueness and estimation of 3-D motion parameters of rigid bodies with curved surfaces,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, pp. 13–27, Jan. 1984.
3. O.D. Faugeras, F. Lustman, and G. Toscani, “Motion and structure from motion from point and line matches,” in Proc. 1st ICCV, London, England, June 1987.
4. J. Weng, T.S. Huang, and N. Ahuja, “Error analysis of motion parameters determination from image sequences,” in Proc. 1st ICCV, London, England, June 1987.
5. C. Braccini, private communication to O.D. Faugeras, Feb. 1987.
6. H.C. Longuet-Higgins, private communication to T.S. Huang, Jan. 29, 1987.
7. T.S. Huang and Y.S. Shim, “Linear algorithm for motion estimation: How to handle degenerate cases,” Coord. Sci. Lab., Univ. Illinois, Urbana, Tech. Note ISP-102, Nov. 30, 1986.
8. H.C. Longuet-Higgins, private communication to T.S. Huang, Mar. 23, 1987.
9. A. Netravali, T.S. Huang, R. Holt, and A.S. Krishnakumar, “Algebraic methods in 3-D motion estimation from two-view point correspondences,” Int. J. Imaging Syst. Technol., to be published.
10. C. Braccini, G. Gambardella, A. Grattarola, and S. Zappatore, “Motion estimation of rigid bodies: Effects of the rigidity constraints,” in Proc. EUSIPCO-86, The Hague, The Netherlands, Sept. 1986. given set of parameters. In structured matching, the knowledge and control for making a decision are integrated within a hierarchical structure. Each node in the hierarchy corresponds to a different aspect of the decision and contains knowledge for directly mapping the results of its children nodes (or selected parameters) into a choice on the sub-decision. The root node selects the final choice for the decision. We formally characterize the task and strategy of structured matching and analyze its computational complexity. Structured matching, we believe, captures the essence of what makes a range of decision-making problems computationally feasible to solve. Computational Feasibility of Structured Matching ASHOK GOEL and TOM BYLANDER Abstract-Structured matching is a task-specific technique for selecting one choice out of a small number of alternatives based on a A. Goel was with the Laboratory for Artificial Intelligence Research, Department of Computer and Information Science, The Ohio State University, Columbus, OH 43210. He is now with the School of Information and Computer Science, Georgia Institute of Technology, Atlanta, GA 30322. T. Bylander is with the Laboratory for Artificial Intelligence Research, Department of Computer and Information Science, The Ohio State University, Columbus, OH 43210.