Feature Matches Filtering Using Geometric Invariants in Image Registration Tasks

Informatica

Volumn 12, Issue 3, 2001, Pages 385-412

  Malickas, Algimantas ^a   Vitkus, Rimantas ^a  

^a VILNIUS UNIVERSITY   (Lithuania)

Author keywords

Composite features; Geometric invariants; Image registration

Indexed keywords

EID: 0345876851     PISSN: 08684952     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (36)

1. Bergen J.R., and H. Shvaytser (1991). A probabilistic algorithm for computing Hough transforms. Journal of Algorithms, 12, 639-656.
2. th Israel Conf, Art. Intell., pp. 35-61.
3. Can A., C.V. Stewart and B. Roysam (1999). Robust hierarchical algorithm for constructing a mosaic from images of the curved human retina. In Proc CVPR, pp. 286-292.
4. Fischler M.A., and Bolles R.C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24, 381-396.
5. Fomin J.A., G.R. Tarlovskij (1986). Statistical theory of pattern recognition. Radio and communications, Moscow (in Russian).
6. Gold S., A. Rangarajan, C. Lu, S. Pappu and E. Mjolsness (1998). New algorithms for 2D and 3D point matching: pose estimation and correspondence, Pattern Recognition, 31(8), 1019-1031.
7. Govindu V., C. Shekhar (1999). Alignment using distributions of local geometric properties. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10).
8. Grimson W.E., and D.P. Huttenlocher (1988). On the sensitivity of the Hough transform for object recognition, IEEE trans. PAMI, 12(3), 255-274; Also in MIT AI lab Memo, 1044, 1988.
9. Grimson W.E., and D.P. Huttenlocher (1988). On the sensitivity of the Hough transform for object recognition, IEEE trans. PAMI, 12(3), 255-274; Also in MIT AI lab Memo, 1044, 1988.
10. Grimson W.E., D.P. Huttenlocher, D.W. Jacobs (1991). Affine matching with bounded sensor error: a study of geometric hashing and alignment. XXX; Also in MIT AI lab Memo, 1250.
11. Grimson W.E., D.P. Huttenlocher, D.W. Jacobs (1991). Affine matching with bounded sensor error: a study of geometric hashing and alignment. XXX; Also in MIT AI lab Memo, 1250.
12. Grimson W.E., D.P. Huttenlocher and T.D. Alter (1992). Recognizing 3D objects from 2D Images: an error analysis., Proc. IEEE conf. On Computer vision and pattern recognition; also MIT AI Lab Memo 1362.
13. Grimson W.E., D.P. Huttenlocher and T.D. Alter (1992). Recognizing 3D objects from 2D Images: an error analysis., Proc. IEEE conf. On Computer vision and pattern recognition; also MIT AI Lab Memo 1362.
14. Hong L. et al. (1998). Fingerprint image enhancement: algorithms and performance evaluation. IEEE Trans. Pattern Analysis and Machine Inteligence, 20(8), 777-789.
15. Hsieh J.W, H.Y. Mark Liao, K.C. Fan, M-T. Ko and Y.P. Hung (1997). Image registration using a new edge-based approach. CVGIP: Computer Vision and Image Understanding, 67(2), 112-130.
16. Hummel R. and H. Wolfson (1988). Affine Invariant Matching. DARPA Image Understanding workshop.
17. Huttenlocher D.P., S. Ullman (1987). Recognizing rigid objects by aligning them with an image. MIT AI lab Memo, 937.
18. Jacobs D.W. (1989). Grouping for recognition. MIT AI Memo, 1177.
19. Lambdan Y.J., J. Schwartz and H. Wolfson (1988). Object recognition by affine invariant matching. In IEEE Computer Vision and Pattern Recognition Conf., pp. 335-344.
20. Lambdan Y.J. and H. Wolfson (1988). Geometric hashing: a general and efficient model based recognition scheme. In Second International Conference on Computer Vision., pp. 238-249.
21. Lester H., Simmon R. (1999). A survey of Hierarchical non-linear medical image registration. Pattern Recognition, 32, 129-149.
22. Levin B.R. (1989). Theoretical foundations of statistical radiotechniques. Radio and Communications, Moscow, p. 653 (in Russian).
23. Lowe D.G. (1987). Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence, 31(3), 355-395.
24. Malickas A., and Vitkus R. (1999). Fingerprint registration using composite features consensus. Informatica, 10(4), 389-402.
25. Olson C.F. (1992). Fast alignment by elimination unlikely matches. Univ. of California at Berkley. Technical Report UCB//CSD-92-704.
26. Olson C.F. (1997). Efficient pose clustering using a randomized algorithm. Int. J. of Computer Vision, 23(2), 131-147.
27. Olson C.F. (2000). A general method for feature matching and model extraction. In Vision Algorithms: Theory and Practice, 20-36.
28. Ratha N., Karu K., Chen S., A. Jain (1996). A real time matching system for large fingerprint database. IEEE Trans. Pattern Analysis and Machine Inteligence, 13(8), 799-813.
29. Shechar Ch., V. Govindu, R. Chellappa (1999). Multisensor image registration by features consensus. Pattern Recognition, 32, 39-52.
30. Tsai F. (1993). A probabilistic approach to geometric hashing using line features. Technical Report 640, Robotics research laboratory, N.Y.U., June.
31. Tsai F.C. 621. Statistical approach to affine invariant matching with line features, N.Y. University, Robotics research lab. TR-621.
32. Weinshall D. (1993). Model-based invariants for 3D vision. Intern. J. of Computer Vision, 10(1), 27-42.
33. Wells W.M. (1993). Statistical object recognition. MIT AI Technical report, 1398.
34. Williamson T.A. (1998). A High Performance Stereo Vision System for Obstacle Detection. Carnegie Mellon Univ. Robotics institute, TR 98-24.
35. Xu L., E. Oja, and P. Kultanen (1990). A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Letters, 11, 331-338.
36. Zheng Q., R. Chellappa (1995). Automatic feature point extraction and tracking in image sequences for arbitrary camera motion. I,J, Computer Vision, 15, 31-76.