Analysis of superimposed oriented patterns

IEEE Transactions on Image Processing

Volumn 15, Issue 12, 2006, Pages 3690-3700

  Aach, Til ^a   Mota, Cicero ^b   Stuke, Ingo ^c   Mühlich, Matthias ^a   Barth, Erhard ^b  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b Federal University of Amazonas   (Germany)

^c UNIVERSITY OF LÜBECK   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; IMAGE PROCESSING; OPACITY; TENSORS;

GRAY-LEVEL VARIANCE AXIS; IMAGE ORIENTATION; MIXED ORIENTATION PARAMETERS;

PATTERN RECOGNITION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 33751364797     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2006.884921     Document Type: Article

References (46)

1. T. Aach and D. Kunz, "Anisotropic spectral magnitude estimation filters for noise reduction and image enhancement," in Proc. Int. Conf. Image Processing, Lausanne, Switzerland, Sep. 16-19, 1996, pp. 335-338.
2. T. Aach and D. Kunz, "A lapped directional transform for spectral image analysis and its application to restoration and enhancement," Signal Process., vol. 80, no. 11, pp. 2347-2364, 2000.
3. W. Freeman and E. Adelson, "The design and use of steerable filters," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 9, pp. 891-906, Sep. 1991.
4. M. Jacob and M. Unser, "Design of steerable filters for feature detection using canny-like criteria," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 8, pp. 1007-1019, Aug. 2004.
5. J. Hladuvka and E. Gröller, "Direction-driven shape-based interpolation of volume data," in Proc. Vision, Modeling and Visualization, Stuttgart, Germany, Nov. 21-23, 2001, vol. 521, pp. 113-120.
6. M. Bertram, G. Rose, D. Schäfer, J.Wiegert, and T. Aach, "Directional interpolation of sparsely sampled cone-beam CT sinogram data," in Proc. IEEE Int. Symp. Biomedical Imaging, Arlington,VA, Apr. 15-18, 2004, pp. 928-931.
7. J. Bigün, G. H. Granlund, and J. Wiklund, "Multidimensional orientation estimation with applications to texture analysis and optical flow," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 8, pp. 775-790, Aug. 1991.
8. J. Bigün, "Frequency and orientation sensitive texture measures using linear symmetry," Signal Process., vol. 29, pp. 1-16, 1992.
9. J. D. Costa, F. L. Pouliquen, C. Germain, and P. Baylou, "New operators for optimized orientation estimation," in Proc. Int. Conf. Image Processing, Oct. 2001, vol. 3, pp. 744-747.
10. F. L. Pouliquen, J. D. Costa, C. Germain, and P. Baylou, "A new adaptive framework for unbiased orientation estimation in textured images," Pattern Recognit., vol. 38, pp. 2032-2046, 2005.
11. G. H. Granlund and H. Knutsson, Signal Processing for Comput. Vision. Dordrecht, The Netherlands: Kluwer, 1995.
12. E. Barth, T. Caelli, and C. Zetzsche, "Image encoding, labeling, and reconstruction from differential geometry," CVGIP: Graph. Model Image Process., vol. 55, no. 6, pp. 428-46, Nov. 1993.
13. C. Mota and E. Barth, "On the uniqueness of curvature features," in Dynamische Perzeption, Proc. Artificial Intelligence, G. Baratoff and H. Neumann, Eds., 2000, vol. 9. Infix, pp. 175-178.
14. B. K. P. Horn and B. G. Schunck, "Determining optical flow," Artif. Intell., vol. 17, pp. 185-203, 1981.
15. H. Knutsson and G. H. Granlund, "Texture analysis using two-dimensional quadrature filters," presented at the IEEE Workshop on Computer Architecture for Pattern Analysis and Image Data Base Management, Pasadena, CA, Oct. 1983.
16. T. Aach, A. Kaup, and R. Mester, "On texture analysis: Local energy transforms versus quadrature filters," Signal Process., vol. 45, no. 2, pp. 173-181, 1995.
17. E. Simoncelli and H. Farid, "Steerable wedge filters for local orientation analysis," IEEE Trans. Image Process., vol. 5, no. 12, pp. 1377-1382, Dec. 1996.
18. S. D. Zenzo, "A note on the gradient of a multi-image," Comput. Vis., Graph., Image Process., vol. 33, pp. 116-125, 1986.
19. J. Bigün and G. H. Granlund, "Optimal orientation detection of linear symmetry," in Proc. IEEE 1st Int. Conf. Computer Vision, London, U.K., Jun. 1987, pp. 433-438.
20. M. Kass and A. Witkin, "Analyzing oriented patterns," Comput. Vis., Graph., Image Process., vol. 37, pp. 362-385, 1987.
21. C. G. Harris and M. J. Stevens, "A combined corner and edge detector," presented at the Proc. 4th Alvey Vision Conf., 1988.
22. W. Förstner, "A feature based corresponding algorithm for image matching," Int. Arch. Photogramm. Remote Sens., vol. 26, pp. 150-166, 1986.
23. U. Köthe, "Edge and junction detection with an improved structure tensor," in Proc. DAGM: 25th Pattern Recognition Symp., B. Michaelis and G. Krell, Eds., Magdeburg, Germany, 2003, vol. LNCS 2781, pp. 25-32.
24. T. Aach, I. Stuke, C. Mota, and E. Barth, "Estimation of multiple local orientations in image signals," in Proc. ICASSP, Montreal, QC, Canada, May 17-21, 2004, pp. III 553-556.
25. B. Rieger and L. J. Vliet, "A systematic approach to nD orientation representation," Image Vis. Comput., vol. 22, pp. 453-459, 2004.
26. E. Trucco and A. Verri, Introductory Techniques for 3D Computer Vision. Upper Saddle River, NJ: Prentice-Hall, 1998.
27. T. Aach, U. Schiebel, and G. Spekowius, "Digital image acquisition and processing in medical x-ray imaging," J. Electron. Imag., vol. 8, pp. 7-22, 1999.
28. J. Hsieh, Computed Tomography. Principles, Design, Artifacts, and Recent Advances. Bellingham, WA: SPIE, 2003.
29. C. Mota, I. Stuke, T. Aach, and E. Barth, "Estimation of multiple orientations at corners and junctions," in Proc. DAGM: 26th Pattern Recognition Symp., Tübingen, Germany, Aug.-Sep. 2004, vol. LNCS 3175, pp. 163-170.
30. M. Felsberg and G. Granlund, "POI detection using channel clustering and the 2D energy tensor," in Proc. DAGM: 26th Pattern Recognition Symp., Tübingen, Germany, Aug.-Sep. 30-1, 2004, vol. LNCS 3175, pp. 103-110.
31. M. Shizawa and K. Mase, "A unified computational theory for motion transparency and motion boundaries based on eigenenergy analysis," in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, Maui, HI, Jun. 3-6, 1991, pp. 289-295.
32. M. Shizawa and T. Iso, "Direct representation and detection of multi-scale, multi-orientation fields using local differentiation filters," in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, Piscataway, NJ, Jun. 1993, pp. 508-514.
33. C. Zetzsche and E. Barth, "Fundamental limits of linear filters in the visual processing of two-dimensional signals," Vis. Res., vol. 30, pp. 1111-1117, 1990.
34. M. Mühlich, "Estimation in Projective Spaces and Applications in Computer Vision," Ph.D. dissertation, Univ. Frankfurt, Frankfurt, Germany, 2005.
35. M. Mühlich and R. Mester, "Optimal estimation of homogeneous vectors," in Proc. 14th Scandinavian Conf. Image Analysis, H. Kalviainen, J. Parkkinen, and A. Kaarna, Eds., Joensuu, Finland, Jun. 19-22, 2005, vol. LNCS 3540, pp. 322-332.
36. D. A. Forsyth and J. Ponce, Computer Vision. A Modern Approach. Upper Saddle River, NJ: Prentice-Hall, 2003.
37. E. Kreyszig, Advanced Engineering Mathematics, 8th ed. Chichester, U.K.: Wiley, 1999.
38. E. Barth, "The minors of the structure tensor," in Proc. Mustererkennung, G. Sommer, Ed., Berlin, Germany, 2000, pp. 221-228.
39. C. Mota, I. Stuke, and E. Barth, "Analytic solutions for multiple motions," in Proc. IEEE Int. Conf. Image Processing, Thessaloniki, Greece, Oct. 7-10, 2001, vol. II, pp. 917-920.
40. C. Mota, T. Aach, I. Stuke, and E. Barth, "Estimation of multiple orientations in multi-dimensional signals," in Proc. IEEE Int. Conf. Image Processing, Singapore, Oct. 24-27, 2004, pp. 2665-2668.
41. T. Aach and D. Kunz, "Bayesian motion estimation for temporally recursive noise reduction in x-ray fluoroscopy," Philips J. Res., vol. 51, no. 2, pp. 231-251, 1998.
42. C. Stiller, "Motion estimation for coding of moving video at 8 kb/s with gibbs modeled vector field smoothing," in Proc. SPIE Visual Communications and Image Processing Conf.,M. Kunt, Ed., Lausanne, Switzerland, Oct. 1990, vol. 1360, pp. 468-476.
43. H. Scharr and B. Jähne, "Optimization of spatio-temporal filter families for fast and accurate motion estimation," in Image Sequence Analysis to Investigate Dynamic Processes. Berlin, Germany: Springer, 2003, LNCS.
44. M. Elad, P. Teo, and Y. Hel-Or, "On the design of optimal filters for gradient-based motion estimation," Int. J. Math. Imag. Vis., vol. 23, no. 3, pp. 245-265, 2005.
45. T. Aach and A. Kaup, "Bayesian algorithms for change detection in image sequences using markov random fields," Signal Process.: Image Commun., vol. 7, no. 2, pp. 147-160, 1995.
46. R. Vidal, Y. Ma, and S. Sastry, "Generalized principal component analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 12, pp. 1-15, Dec. 2005.