Motion analysis: Model selection and motion segmentation

Proceedings - 12th International Conference on Image Analysis and Processing, ICIAP 2003

Volumn , Issue , 2003, Pages 442-448

  Gheissari, Niloofar ^a   Bab Hadiashar, Alireza ^a  

^a SWINBURNE UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

3D probability density; camera calibration; eye tracking; gazed position measurement

Indexed keywords

CAMERA CALIBRATION; EYE-TRACKING; MODEL SELECTION CRITERIA; MOTION SEGMENTATION; PHYSICAL CHARACTERISTICS; PROBABILITY DENSITIES; REAL IMAGE SEQUENCES; STATISTICAL MODELING;

IMAGE ANALYSIS; IMAGE SEGMENTATION;

MODELS;

EID: 84890449173     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIAP.2003.1234090     Document Type: Conference Paper

References (42)

1. Adiv, G., Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects, IEEE transactions on pattern analysis and machine intelligence, vol. 7, pp. 384-401, Jul, 1985.
2. Akaike, H., A New Look at the Statistical Model Identification, IEEE Trans. on Automatic Control, vol. AC 19, pp. 716-723, 1974.
3. Bab-Hadiashar, A. and Suter, D., Robust Segmentation of Visual Data Using Ranked Unbiased Scale Estimate, ROBOTICA, International Journal of Information, Education and Research in Robotics and Artificial Intelligence, vol. 17, pp. 649-660, 1999.
4. Bab-Hadiashar, A., Gheissari, N., and Suter, D., Robust Model Based Motion Segmentation, ICPR 2002, Quebec,Canada, pp. 753-757, Aug. 2002.
5. Banfield, J. and Raftery, A. E., Model-based Gaussian and non-Gaussian clustering., Biometrics, vol. 49, pp. 803-821, 1993.
6. Barron, J. L., Fleet, D. J., Beauchemin, S. S., and Burkitt, T. A., Performance of Optical Flow Techniques, Proceedings of CVPR'92, Washington, pp. 236-42, 1992.
7. Besag, J., On the Statistical Analysis of Dirty Pictures, Journal of the Royal Statistical Society, vol. 48, pp. 259-302, 1986.
8. Bober, M. and Kittler, J., Estimation of Complex Multimodel Motion: an Approach Based on Robust Statistics and Hough Transform., Image and Vision Computing, vol. 12, pp. 661-668, 1994.
9. Bozdogan, H., Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions, Psychometrica, vol. 52, pp. 345-370, 1987.
10. Bubna, K. and Stewart, V. C., Model Selection and Surface Merging in Recognition Algorithms, ICCV 98, Bombay, India, pp. 895-902, 1998.
11. Celeux, G. and Govaert, G., Gaussian Parsimonious Clustering Models, The Journal of the Pattern Recognition Society, vol. 28, pp. 781-793, 1995.
12. Chickering, D. and Heckerman, D., Efficient Approximation for the Marginal Likelihood of Bayesian Networks with Hidden Variables, Machine Learning, vol. 29, no. 2-3, pp. 181-212, 1997.
13. Dasgupta, A. and Raftery, A. E., Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering., Journal of the American Statistical Association, vol. 93, pp. 294-302, 1998.
14. Duncan, J. S., Lee, F. A., Smeulders, A. W. M., and Zaret, B. L., A Bending Energy Model for Measurement of Cardic Shape Deformity, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, pp. 307-319, 1991.
15. Geman, S. and Geman, D., Stochastic Relaxations, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 6, pp. 731-741, 1984.
16. Gordon, A. D. Classification: Methods for the Exploratory Analysis of Multivariate Data, London:Chapman and Hall,1981.
17. Hartigan, J. A. Clustering Algorithms, New York:Wiley &Sons,1975.
18. Hotter, M. and Thoma, R., Image Segmentation Based on Object Oriented Mapping Parameter Estimation, Signal processing, vol. 15, pp. 315-334, 1988.
19. Kanatani, K., What Is the Geometric AIC? Reply to My Reviewers, 1987(unpublished).
20. Kanatani, K., Geometric Information Criterion for Model Selection, IJCV, vol. 26, pp. 171-189, 1998.
21. Kanatani, k., Motion Segmentation by Subspace Separation and Model Selection, Proceedings of the 8th International Conference on Computer Vision, (ICCV 2001), Vancouver,Canada, pp. 301-306, July 2001.
22. Kanatani, k., Evaluation and selection of models for motion segmentation. Memoirs of the Faculty of Engineering, Okayama University, vol. 36, pp. 91-106, Dec, 2001.
23. Kanatani, K., Model Selection for Geometric Inference, The 5th Asian Conference on Computer Vision, Melbourne, Australia, pp. xxi-xxxii, January 2002.
24. Kass, M., Witkin, A., and Terzopoulos, D. Snakes: Active Contour Models. In: Anonymous 1987.pp. 269-276.
25. Mallows, C. L., Some Comments on CP, Technometrics, vol. 15, pp. 661-675, 1973.
26. Marroquin, J., Mitter, S., and Poggio, T., Probabilistic Solution of Ill-Posed Problems in Computational Vision, J. American Statistical Assoc., vol. 82, pp. 76-89, 1987.
27. McLachlan, G. I. and Basford, K. Mixture models: inference and applications to clustering, New York:Marcel Dekker,1988.
28. Mech, R. W. M., A Noise Robust Method for Segmentation of Moving Objects In Video Sequences, ICASSP, Munich, Germany, pp. 2657-2660, 1997.
29. Mignotte, M., Collet, C., and Pe'rez, P., Unsupervised Hierarchical Markovian Segmentation of Sonar Images, 4th IEEE Int. Conf. Image Processing, ICIP'97, Washington, DC, pp. 2781-2785,1997.
30. Murtagh, F. Multidimensional Clustering Algorithms, Heidelberg:Physica- Verlag,1985.
31. Noah S. Friedland, PTBS Segmentation Scheme for Synthetic Aperture Radar., Proceedings of the SPIE, Signal Processing, Sensor Fusion, and Target Recognition IV, pp. 476-493, July 1995.
32. Rissanen, J., Universal Coding, Information, Prediction and Estimation, IEEE Trans. Inf. Theory, vol. 30, pp. 629-636, 1984.
33. Rissanen, J., Modeling by Shortest Data Description, Automatica, vol. 14, pp. 465-471, 1978.
34. Ronchetti, E., Robust model selection in regression, Statistics and Probability Letters, vol. 3, pp. 21-23, 1985.
35. S., T. P. H., Geometric motion segmentation and model selection, Phil. Trans. R. Soc. Lond. A, vol. 356, pp. 1321-1340, 1998.
36. Tian, T. Y. and Shah, M., Estimating 3-D Motion and Shape of Multiple Objects Using Hough Transform, ICPR 94, Jerusalem, Israel, pp. 658-660, 1994.
37. Timoshenko, p. S. and Krieger, S. W. Theory of Plates and Shells. In: Chapter 2, Pure Bending of Plates, eds. Timoshenko, p. S. and Krieger, S. W. McGraw-Hill, 1959.pp. 46-47.
38. Tina, Y. and Shah, M., Recovering 3d Motion of Multiple Objects Using Adaptive Hough Transform, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 10, pp. 1178-1183, 1997.
39. Tissainayagam, P. and Suter, D. Visual Feature Tracking with Automatic Motion Model Selection. In: Anonymous1998.pp. 322-325.
40. Torr, P. H. S., An assessment of information criteria for motion model selection, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 47-53, 1997.
41. Torr, P. H. S., Model Selection for Two View Geometry: A Review, Model Selection for Two View Geometry: A Review, Microsoft Research, USA, Microsoft Research, USA, 1998.
42. Wang, J. and Adelson, E., Representing Moving With Layers, IEEE TPAMI, vol. 3, pp. 625-638, 1994.