Segmentation of Textured Images and Gestalt Organization Using Spatial/Spatial-Frequency Representations

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 12, Issue 1, 1990, Pages 1-12

  Reed, Todd R ^a   Wechsler, Harry ^b  

^a EPFL   (Switzerland)

^b GEORGE MASON UNIVERSITY   (United States)

Author keywords

Clustering; DOG; Gabor; Gestalt; Grouping; Relaxation; Spatial spatial frequency (s sf) representations; Spectrogram; Texture segmentation; Wigner

Indexed keywords

MATHEMATICAL TRANSFORMATIONS--FOURIER TRANSFORMS; SPECTRUM ANALYSIS; VISION;

DIFFERENCE OF GAUSSIANS; GABOR REPRESENTATION; GESTALT GROUPING; SPATIAL/SPATIAL-FREQUENCY REPRESENTATIONS; TEXTURED IMAGES SEGMENTATION; WIGNER DISTRIBUTION;

IMAGE PROCESSING;

EID: 0025256165     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.41379     Document Type: Article

References (65)

1. R. W. Ehrich and J. P. Foith, “A view of texture topology and texture description,” Comput. Graphics Image Processing, vol. 8, no. 2, pp. 174–202, Oct. 1978.
2. T. Pavlidis, Structural Pattern Recognition. New York: Springer-Verlag, 1977, ch. 4, pp. 68–70.
3. E. M. Gurari and H. Wechsler, “On the difficulties involved in the segmentation of pictures,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-4, no. 3, pp. 304–306, May 1982.
4. M. Wertheimer, “Principles of perceptual organization,” in Readings in Perception, D. C. Beardslee and M. Wertheimer, Eds. Princeton NJ: Van Nostrand, 1958, pp. 115–135.
5. A. Gagalowicz, “A new method for texture field synthesis: Some applications to the study of human vision,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-3, no. 5, pp. 520–533, Sept. 1981.
6. B. Julesz and J. Bergen, “Textons, the fundamental elements in preattentive vision and perception of textures,” Bell Syst. Tech. J., vol. 62, no. 2, pp. 1619–1645, July-Aug. 1983.
7. T. Caelli, “Three processing characteristics of visual texture segmentation,” Spatial Vision, vol. 1, no. 1, pp. 19–30, 1985.
8. R. W. Connors and C. A. Harlow, “A theoretical comparison of texture algorithms,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-2, no. 3, pp. 204–222, May 1980.
9. J. S. Weszka, C. R. Dyer, and A. Rosenfeld, “A comparison study of texture measures for terrain classification,” IEEE Trans. Syst., Man, Cybern., vol. 6, no. 4, pp. 269–286, 1976.
10. S. W. Zucker and P. Cavanagh, “Subjective figures and texture perception,” Comput. Vision Robotics Lab., Dep. Elec. Eng., McGill Univ., Tech. Rep. TR-85-2R, Feb. 1985.
11. D. Gabor, “Theory of communication,” Proc. IEE, vol. 93, no. 26, pp. 429–457, 1946.
12. C. E. Shannon, “Communication in the presence of noise,” in Proc. IRE, vol. 37, pp. 10–21, Jan. 1949.
13. J. G. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” J. Opt. Soc. Amer. A, vol. 2, no. 7, pp. 1160–1169, July 1985.
14. R. Wilson and G. H. Granlund, “The uncertainty principle in image processing,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, no. 6, pp. 758–767, Nov. 1984.
15. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst., Man, Cybern., vol. 3, no. 1, pp. 610–621, Nov. 1973.
16. S. Geman and D. Geman, “Stoachstic relaxation, Gibbs distributions, and Bayesian restoration of images,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, no. 6, pp. 721–741, Nov. 1984.
17. H. Derin and H. Elliot, “Modeling and segmentation of noisy and textured images using Gibbs random fields,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9, no. 1, pp. 39–55, Jan. 1987.
18. F. H. C. Crick, D. C. Marr, and T. Poggio, “An information processing approach to understanding the visual cortex,” M.I.T., A. I.
19. A. B. Watson, “The cortex transform: Rapid computation of simulated neural images,” Comput. Vision, Graphics, Image Processing, vol. 39, pp. 311–327, 1987.
20. A. P. Ginsburg, “Specifying relevant spatial information for image evaluation and display design: An explanation of how we see certain objects,” Proc. SID, vol. 21, no. 3, pp. 219–227, 1980.
21. J. Beck, A. Sutter, and R. Ivry, “Spatial frequency channels and perceptual grouping in texture segregation,” Comput. Vision, Graphics, Image Processing, vol. 37, pp. 299–325, 1987.
22. L. Jacobson and H. Wechsler, “Joint spatial/spatial-frequency representation,” Signal Processing, vol. 14, no. 1, pp. 37–68, Jan. 1988.
23. J. B. Allen and L. R. Rabiner, “A unified approach to short-time Fourier analysis and synthesis,” Proc. IEEE, vol. 65, no. 11, pp. 1558–1564, Nov. 1977.
24. R. Bajcsy and L. Lieberman, “Texture gradient as a depth cue,” Comput. Graphics Image Processing, vol. 5, pp. 52–61, 1976.
25. A. P. Pentland, “Fractal-based description of natural scenes,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, no. 6, pp. 661–674, Nov. 1984.
26. H. Wilson and S. C. Giese, “Threshold visibility of frequency gradient patterns,” Vision Res., vol. 17, pp. 1177–1190, 1977.
27. A. P. Witkin, “Scale-space filtering,” in Proc. 8th Int. Joint Conf. Artificial Intelligence, Karlsruhe, West Germany, Aug, 1983, pp. 1019–1022.
28. C. H. Anderson, P. J. Burt, and G. S. van der Wal, “Change detection and tracking using pyramid transform techniques,” in Proc. SPIE/ Cambridge Int. Conf. Intelligent Robots and Computer Vision. Cambridge, MA. Sept. 1985, pp. 72–78.
29. A. Rosenfeld, “Some useful properties of pyramids.” in Multiresolution Image Processing and Analysis, A. Rosenfeld, Ed. Berlin: Springer-Verlag, 1984.
30. P. Meer, E. S. Baugher, and A. Rosenfeld, “Frequency domain analysis and synthesis of image pyramid generating kernels,” IEEE Trans. Pattern Anal. Machine Intell., vol. 9, no. 4, pp. 512–522, July 1987.
31. J. L. Crowley and A. C. Parker, “A representation for shape based on peaks and ridges in the difference of low-pass transform,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, no. 2, pp. 156–170, Mar. 1984.
32. D. Marr, Vision. New York: Freeman, 1982, p. 64.
33. M. Porat and Y. Y. Zeevi, “The generalized Gabor scheme of image representation in biological and machine vision,” IEEE Trans. Pattern Anal. Machine Intell., vol. 10, no. 4, pp. 452–468, July 1988.
34. M. Clark, A. C. Bovik, and W. S. Geisler, “Texture segmentation using a class of narrowband filters,” in Proc. Int. Conf. Acoustics, Speech, and Signal Processing, Apr. 1987, pp. 14.6.1–14.6.4.
35. J. J. Kulikowski, S. Marcelja, and P. O. Bishop, “Theory of spatial position and spatial frequency relations in the receptive fields of simple cells in the visual cortex,” Biol. Cybern., vol. 43, pp. 187–198, 1982.
36. D. A. Pollen and S. F. Ronner, “Phase relationships between adjacent simple cells in the visual cortex,” Science, vol. 212, pp. 1409–1411, 1981.
37. E. Wigner, “On the quantum correction for thermodynamic equilibrium,” Phys. Rev., vol. 40, pp. 749–759, June 1932.
38. J. Ville, “Theorie et applications de la notion de signal analytique,” Cable et Transmission, vol. 2, no. 1, pp. 61–74, 1948.
39. T. A. C. M. Claasen and W. F. G. Mecklenbrauker, “The Wigner distribution—A tool for time-frequency signal analysis, Part I: Continuous-time signals,” Philips J. Res., vol. 35, no. 3, pp. 217–250, 1980.
40. T. A. C. M. Claasen and W. F. G. Mecklenbrauker, “The Wigner distribution—A tool for time-frequency signal analysis. Part II: Discrete-time signals,” Philips J. Res., vol. 35, no. 4/5, pp. 276–300, 1980.
41. T. A. C. M. Claasen and W. F. G. Mecklenbrauker, “The Wigner distribution—A tool for time-frequency signal analysis. Part III: Relations with other time-frequency signal transformations,” Philips J. Res., vol. 35, no. 6, pp. 372–389, 1980.
42. H. O. Bartelt, K. H. Brenner, and A. W. Lohmann, “The Wigner distribution function and its optical production,” Opt. Commun., vol. 32, no. 1, pp. 32–38, Jan. 1980.
43. M. J. Bastiaans, “The Wigner distribution function applied to optical signals and systems,” Opt. Commun., vol. 25, no. 1, pp. 26–30, Apr. 1978.
44. M. J. Bastiaans, “Wigner distribution function and its application to first-order optics,” J. Opt. Soc. Amer., vol. 69, no. 12, pp. 1710–1716, Dec. 1979.
45. L. Jacobson and H. Wechsler, “A paradigm for invariant object recognition of brightness, optical flow and binocular disparity images,” Pattern Recognition Lett., vol. 1, pp. 61–68, Oct, 1982.
46. L. Jacobson and H. Wechsler, “A theory for invariant object recognition in the frontoparallel plane,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, no. 3, pp. 325–331, May 1984.
47. L. Jacobson and H. Wechsler, “Derivation of optical flow using a spatiotemporal-frequency approach,” Comput. Vision, Graphics, Image Processing, vol. 38, pp. 29–65, 1987.
48. A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” Proc. IEEE, vol. 69, pp. 529–541, 1981.
49. W. Martin and P. Flandrin, “Analysis of non-stationary processes: Short-time periodigrams versus a pseudo-Wigner estimator,” in EUSIPCO-83, H. Schussler, Ed. Amsterdam, The Netherlands: North-Holland, 1983, pp. 455–458.
50. W. Martin, “Measuring the degree of non-stationarity by using the Wigner-Ville spectrum,” in Proc. Int. Conf. Acoustics, Speech, and Signal Processing, San Diego, CA, Mar. 19–21 1984, pp. 41B.3–41B.4.
51. W. Martin and P. Flandrin, “Detection of changes of signal stracture by using the Wigner-Ville spectrum,” Signal Processing, vol. 8, no. 2, pp. 215–233, 1985.
52. T. R. Reed and H. Wechsler, “Tracking of non-stationarities for texture fields,” Signal Processing, vol. 14, no. 1, pp. 95–102, Jan. 1988.
53. L. Cohen, “Generalized phase-space distribution functions,” J. Math. Phys., vol. 7, no. 5, pp. 781–786, May 1966.
54. W. Martin and P. Flandrin, “Wigner-Ville spectral analysis of nonstationary processes,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-33, no. 6, pp. 1461–1470, Dec. 1985.
55. G. F. Boudreaux-Bartels and T. W. Parks, “Reducing aliasing in the Wigner distribution using implicit spline interpolation,” in Proc. Int. Conf. Acoustics, Speech, and Signal Processing-83, Boston, MA, Apr. 1983, pp. 1438–1441.
56. F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proc. IEEE, vol. 66, no. 1, pp. 51–83, Jan. 1978.
57. J. F. Kaiser, “Nonrecursive digital filtering using the I0-sinh window function,” in Proc. IEEE Int. Symp. Circuit Theory, 1974, pp. 20–23.
58. R. Wilson, “Finite prolate spheroidal sequences and their applications I: generation and properties,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9, no. 6, pp. 787–795, Nov. 1987.
59. A. Rosenfeld, “Iterative methods in image analysis,” Pattern Recognition, vol. 10, pp. 181–187, 1978.
60. P. Brodatz, Textures—A Photographic Album for Artists and Designers. New York: Dover, 1966.
61. J. Beck, “Textural segmentation, second-order statistics, and textural elements,” Biol. Cybern., vol. 48, pp. 125–130, 1983.
62. T. Terzopoulos, “Image analysis using multigrid relaxation methods,” IEEE Trans. Pattern Ana! Machine Intell., vol. PAMI-8, no. 2, pp. 129–139, Mar. 1986.
63. F. P. Preparata and M. I. Shamos, Computational Geometry—An Introduction. New York: Springer-Verlag, 1985, ch. 5, pp. 181–183.
64. A. Treisman, “Preattentive processing in vision,” Comput. Vision, Graphics, Image Processing, vol. 31, no. 2, pp. 156–177, Aug. 1985.
65. L. Jáñez, “Visual grouping without low spatial frequencies,” Vision Res., vol. 24, no. 3, pp. 271–274. 1984.