Parameter Estimation for Optimal Object Recognition: Theory and Application

International Journal of Computer Vision

Volumn 21, Issue 3, 1997, Pages 207-222

  Li, Stan Z ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA STRUCTURES; LEARNING SYSTEMS; OPTIMIZATION; PARAMETER ESTIMATION; SYSTEM STABILITY;

CORRECTNESS; ENERGY FUNCTION; OPTIMAL OBJECT RECOGNITION; SUPERVISED LEARNING;

OBJECT RECOGNITION;

EID: 0031070582     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007947800092     Document Type: Article

References (42)

1. Baddeley, A.J. and van Lieshout, M.N.M. 1992. Object recognition using Markov spatial processes. In Proceedings of IEEE International Conference Pattern Recognition, B:136-139.
2. Besag, J. 1974. Spatial interaction and the statistical analysis of lattice systems (with discussions). Journal of the Royal Statistical Society, Series B, 36:192-236.
3. Besag, J. 1975. Statistical analysis of non-lattice data. The Statistician, 24(3):179-195.
4. Bhanu, B. and Faugeras, O.D. 1984. Shape matching of two-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(2):137-155.
5. Boser, B.E., Guyon, I.M., and Vapnik, V.N. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania.
6. Cohen, F.S. and Cooper, D.B. 1987. Simple parallel hierarchical and relaxation algorithms for segmenting noncasual Markovian random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(2):195-218.
7. Cooper, P.R. 1990. Parallel structure recognition with uncertainty: Coupled segmentation and matching. In Proceedings of IEEE International Conference on Computer Vision, pp. 287-290.
8. Cross, G.C. and Jain, A.K. 1983. Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(1):25-39.
9. Davis, L.S. 1979. Shape matching using relaxation techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(1):60-72.
10. Derin, H. and Elliott, H. 1987. Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(1):39-55.
11. Duda, R.O. and Hart, P.E. 1973. Pattern Classification and Scene Analysis. Wiley.
12. Fan, T.J., Medioni, G., and Nevatia, R. 1989. Recognizing 3D objects using surface descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(11):1140-1157.
13. Fischler, M. and Elschlager, R. 1973. The representation and matching of pictorial structures. IEEE Transactions on Computers, C-22:67-92.
14. Geiger, D. and Poggio, T. 1987. An optimal scale for edge detection. In Proc. International Joint Conference on AI.
15. Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6):721-741.
16. Ghahraman, D.E., Wong, A.K.C., and Au, T. 1980. Graph optimal monomorphism algorithms. IEEE Transactions on Systems, Man and Cybernetics, SMC-10(4):181-188.
17. Grimson, W.E.L. 1990. Object Recognition by Computer-The Role of Geometric Constraints. MIT Press: Cambridge, MA.
18. Jacobus, C.J., Chien, R.T., and Selander, J.M. 1980. Motion detection and analysis of matching graphs of intermediate level primitives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(6):495-510.
19. Kim, I.Y. and Yang, H.S. 1992. Efficient image understanding based on the Markov random field model and error backpropagation network. In Proceedings of IEEE International Conference Pattern Recognition, A:441-444.
20. Li, S.Z. 1993. Similarity invariants for 3D space curve matching. In Proceedings of the First Asian Conference on Computer Vision, Osaka, Japan.
21. Li, S.Z. 1994. A Markov random field model for object matching under contextual constraints. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, Washington.
22. Li, S.Z. 1995. Markov Random Field Modeling in Computer Vision. Springer-Verlag.
23. Marroquin, J.L. 1985. Probabilistic Solution of Inverse Problems. Ph.D. thesis, MIT AI Lab.
24. Modestino, J.W. and Zhang, J. 1989. A Markov random field model-based approach to image interpretation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 458-465.
25. Nadabar, S.G. and Jain, A.K. 1992. Parameter estimation in MRF line process models. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 528-533.
26. Nasrabadi, N., Li, W., and Choo, C.Y. 1990. Object recognition by a Hopfield neural network. In Proceedings of Third International Conference on Computer Vision, Osaka, Japan, pp. 325-328.
27. Oshima, M. and Shirai, Y. 1983. Object recognition using three-dimensional information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(4):353-361.
28. Pelillo, M. and Refice, M. 1994. Learning compatibility coefficients for relaxation labeling processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(9):933-945.
29. Poggio, T. and Edelman, S. 1990. A network that learn to recognize three-dimensional objects. Nature, 343:263-266.
30. Pope, A.R. and Lowe, D.G. 1993. Learning object recognition models from images. In Proceedings of IEEE International Conference on Computer Vision, pp. 296-301.
31. Qian, W. and Titterington, D.M. 1989. On the use of Gibbs Markov chain models in the analysis of image based on second-order pairwise interactive distributions. Journal of Applied Statistics, 16(2):267-281.
32. Rosenblatt, F. 1962. Principles of Neurodynamics. Spartan: New York.
33. Shahraray, B. and Anderson, D. 1989. Optimal estimation of contour properties by cross-validated regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11:600-610.
34. Shapiro, L.G. and Haralick, R.M. 1981. Structural description and inexact matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3:504-519.
35. Thompson, A.M., Brown, J.C., Kay, J.W., and Titterington, D.M. 1991. A study of methods of choosing the smoothing parameter in image restoration by regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13:326-339.
36. Vapnik, V. 1982. Estimation of Dependences Based on Empirical Data. Springer-Verlag: New York.
37. Wahba, G. 1980. Spline bases, regularization, and generalized cross-validation for solving approximation problems with large quantities of noisy data. In Approximation Theory III, E. Cheney (Ed.), Academic Press, vol. 2.
38. Wells III, W.M. 1991. MAP model matching. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 486-492.
39. Weng, J., Ahuja, N., and Huang, T.S. 1992. Matching two perspective views. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:806-825.
40. Weng, J.J., Ahuja, N., and Huang, T.S. 1993. Learning recognition and segmentation of 3-d objects from 2-d images. In Proceedings of IEEE International Conference on Computer Vision, pp. 121-128.
41. Wong, A.K.C. and You, M. 1985. Entropy and distance of random graphs with application of structural pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(5):599-609.
42. Zhang, J. 1988. Two Dimensional Stochastic Model-Based Image Analysis. Ph.D. Thesis, Rensselaer Polytechnic Institute.