Statistical Model-Based Algorithms for Image Analysis

Proceedings of the IEEE

Volumn 74, Issue 4, 1986, Pages 532-551

  Therrien, Charles W ^a   Quatieri, Thomas F ^b   Dudgeon, Dan E ^b  

^a NAVAL POSTGRADUATE SCHOOL   (United States)

^b MIT LINCOLN LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE OBJECT DETECTION; IMAGE REGION CLASSIFICATION; IMAGE SEGMENTATION; STATISTICAL MODEL-BASED ALGORITHMS; SYSTOLIC ARRAYS;

MATHEMATICAL MODELS; MATHEMATICAL TECHNIQUES - ALGORITHMS; SIGNAL FILTERING AND PREDICTION;

IMAGE PROCESSING;

COMPUTER ANALYSIS; COMPUTER PROGRAM; IMAGE ANALYSIS; NONBIOLOGICAL MODEL; NONHUMAN; STATISTICS;

EID: 0022695835     PISSN: 00189219     EISSN: 15582256     Source Type: Journal    
DOI: 10.1109/PROC.1986.13504     Document Type: Article

References (53)

1. W. K. Pratt, Digital Image Processing. New York: Wiley, 1978.
2. C. W. Therrien, “An estimation-theoretic approach to terrain image segmentation,” Comput. Vision, Graphics, Image Processing, vol. 22, no. 3, 313–326, June 1983.
3. T. F. Quatieri, “Object detection by two-dimensional linear prediction,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (Boston, MA, Apr. 14–16, 1983).
4. “Object detection by two-dimensional linear prediction,” Tech. Rep. 632, MIT Lincoln Lab., DTIC AD-A126340/9, Jan. 1983.
5. B. H. McCormick and S. N. Jayaramamurthy, “Time series model for texture synthesis,” Int. J. Comput. Inf. Sci., vol. 3, 1974.
6. W. K. Pratt, O. D. Faugeras, and A. Gagalowicz, “Applications of stochastic texture field models to image processing,” Proc. IEEE, vol. 69, pp. 542–551, 1981.
7. O. D. Faugeras, “Autoregressive modeling with conditional expectations for texture synthesis,” in Proc. 5th Int. Conf. on Pattern Recognition (Miami Beach, FL, Dec. 1–4, 1980).
8. W. K. Pratt and O. D. Faugeras, “Development and evaluation of stochastic-based visual texture features,” in Proc. 4th Int. Conf. on Pattern Recognition (Kyoto, Japan, Nov. 7–10, 1978).
9. A. Gagalowicz, “Analysis of texture using a stochastic model,” in Proc. 4th Int. Conf. on Pattern Recognition (Kyoto, Japan, Nov. 7–10, 1978).
10. K. I. Laws, “Rapid texture identification,” Proc. Soc. Photo-Opt. Instrum. Eng., vol. SPIE-23, pp. 376–380, 1980.
11. J. T. Tou and Y. S. Chang, “An approach to texture pattern analysis and recognition,” in Proc. IEEE Conf. on Decision and Control (Clearwater, FL, Dec. 1976).
12. C. W. Therrien, “Linear filtering models for texture classification and segmentation,” in Proc. 5th Int. Conf. on Pattern Recognition (Miami Beach, FL, Dec. 1–4, 1980).
13. C. H. Chen et al. “Some experimental results on linear estimation for image analysis,” Tech. Rep. SMU-EE-TR-84-4, Jan. 1981.
14. D. B. Cooper et al., “Stochastic boundary estimation and object recognnrskition,” Comput. Vision, Graphics, Image Processing, vol. 12, pp. 326–356, 1980.
15. D. B. Cooper, “Maximum likelihood estimation of Markov-process blob boundaries in noisy images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-1, pp. 372–384, Oct. 1979.
16. H. Elliott et al., “Implementation, interpolation, and analysis of a suboptimal boundary finding algorithm,” in Proc. IEEE Computer Society Conf. on Pattern Recognition Image Processing (Chicago, IL, Aug. 6–8).
17. F. R. Hansen and H. Elliott, “Image segmentation using simple Markov field models,” Comput. Vision, Graphics Image Processing, vol. 20, pp. 101–132, Oct. 1982.
18. J. F. Claerbout, “Detection of P-waves from weak sources at great distances,” Geophys., vol. 24, no. 2, pp. 197–211, Apr. 1984.
19. G. A. Clark and P. W. Rodgers, “Adaptive prediction applied to seismic event detection,” Proc. IEEE, vol. 69, no. 9, pp. 1166–1168, Sept. 1981.
20. C. D. Wang, “Adaptive spatial temporal/spectral filters for background clutter suppression and target detection,” Opt. Eng., vol. 21, no. 6, pp. 1033–1038, Dec. 1982.
21. W. P. Dove and A. V. Oppenheim, “Event location using recursive least squares signal processing,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (Denver, CO, Apr. 1980).
22. D. T. L. Lee and M. Morf, “A novel innovations-approach based time-domain pitch detector,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (Denver, CO, Apr. 1980).
23. H. T. Kung and C. E. Leiserson, in C. Mead and L. Conway, Introduction to VLSI Systems, Reading, MA: Addision-Wesley, 1980, sec. 8.3.
24. C. W. Therrien, “A systolic array structure for target detection,” in Proc. 77th Annu. Asilomar Conf. on Circuits, Systems, and Computers (Pacific Grove, CA, Oct. 31-Nov. 2, 1983).
25. T. L. Marzetta, “Two-dimensional linear prediction: Autocorrelation arrays, minimum-phase prediction error filters, and reflection coefficient arrays,” IEEE Trans. Acoust. Speech, Sig-nal Processing, vol. ASSF1-28, pp. 726–733, Dec. 1980.
26. M. P. Ekstrom and J. W. Woods, “2-D spectral factorization with applications in recursive digital filtering,” IEEE Trans. Acoust. Speech, Signal Processing, vol. ASSP-24, no. 2, pp. 115–178, Apr. 1976.
27. D. Dudgeon and R. Mersereau, Multidimensional Digital Sig-nal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1984.
28. H. Chang and J. K. Aggarwal, “Design of two-dimensional semicausal recursive filters,” IEEE Trans. Circuits Syst., vol. CAS-25, no. 12, pp. 1051–1059, Dec. 1978.
29. M. Wax and T. Kailath, “Efficient inversion of Toeplitz-block Toeplitz matrix,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-31, no. 5, pp. 1218–1222, Oct. 1983.
30. A. V. Oppenheim and R. W. Schafer, Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1975.
31. S. M. Kay and S. L. Marple, Jr., “Spectrum analysis—A modern perspective,” Proc. IEEE, vol. 69, no. 11, pp. 1380–1419, Nov. 1981.
32. J. Makhoul, “Linear prediction: A tutorial review,” Proc. IEEE, vol. 63, pp. 561–580, Apr. 1975.
33. H. L. Van Trees, Detection, Estimation, and Modulation Theory, vol. 1. New York: Wiley, 1968.
34. A. Drake, Fundamentals of Applied Probability Theory. New York: McGraw-Hill, 1987.
35. C. W. Therrien, “On the relation between triangular matrix decomposition and linear prediction,” Proc. IEEE, vol. 71, no. 12, pp. 1459–1460, Dec. 1983.
36. J. Besag, “Spatial interaction and the statistical analysis of lattice systems,” I. Roy. Statist. Soc., ser. B., vol. 36, pp. 192–236, 1974.
37. A. Rosenfeld, R. A. Hummel, and S. W. Zucker, “Scene labeling by relaxation operations,” IEEE Trans. Syst. Man Cybern., vol. SMC-6, pp. 420–433, 1976.
38. R. A. Hummel and S. W. Zucker, “On the foundations of relaxation labeling processes,” in Proc. 5th Int. Conf. on Pattern Recognition (Miami, FL, Dec. 1–4, 1980), pp. 50–63.
39. U. Weiser and A. Davis, “A wavefront notational tool for VLSI array design,” in Proc. SMU Conf. on VLSI Systems and Computations, H. T. Kung, B. Sproull, and G. Steele, Eds. Rockville, MD: Comput. Sci. Press, 1981.
40. H. T. Kung and M. S. Lam, “Fault-tolerant VLSI systolic arrays and two level pipelining,” Proc. SPIE, Soc. Photo-Opt. Instrum. Eng., vol. 431, pp. 143–158, 1983.
41. W. M. Gentleman and H. T. Kung, “Matrix triangularization by stystolic arrays,” in Proc. SPIE, Soc. Photo-Opt. Instrum. Eng. (Real Time Signal Processing IV), 1981.
42. S. L. Garverick and E. A. Pierce, “A single wafer 16-point 16 MHz FFT processor,” in Proc. IEEE 7993 Custom Integrated Circuits Conf. (Rochester, NY, May 1983), pp. 23–25.
43. J. I. Raffel, “A demonstration of very large area integration using laser restructuring,” in Proc. IEEE Int. Symp. on Circuits and Systems (Newport Beach, May 1983).
44. M. D. Richards, C. W. Therrien, and J. S. Lim, “Image segmentation using spatial linear prediction,” in Proc. IEEE Int. Cool. on Acoustics, Speech, and Signal Processing (San Diego, CA, Mar. 1984).
45. J. E. Bevington and R. M. Mersereau, “A maximum-likelihood approach to image segmentation by texture,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (San Diego, CA, Mar. 1984).
46. A. K. Jain, “Advances in mathematical models for image processing,” Proc. IEEE, vol. 69, no. 5, pp. 502–528, May 1981.
47. J. W. Woods, “Image detection and estimation,” in Digital Image Processing Techniques, M. P. Ekstrom, Ed. New York: Academic Press, 1984, ch. 3
48. H. Kaufman, J. W. Woods, V. K. Ingle, R. Mediavilla, and A. Radpour, “Recursive image estimation: A multiple model approach,” in Proc. lath Conf. on Decision and Control (Fort Lauderdale, FL, Dec. 12–14, 1979).
49. J. P. Gambotto, “Two-dimensional time series for textures in digital image processing,” in J. C. Simon and R. M. Haralick, Eds., Digital Image Processing (Proc. of NATO Advanced Study Institute, Bonas, France, June-July, 1980). Hingham, MA: D. Reidal, 1981, pp. 211–229.
50. F. S. Cohen and D. B. Cooper, “Real-time texture image segmentation based on noncausal Markovian random field modefs,” Proc. SP1E, vol. 449, pp. 17–28, 1984.
51. S. Rangenatha and A. K. Jain, “Two-dimensional linear prediction models—Part I: Spectral factorization and realization,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-33, no. 1, Feb. 1985.
52. M. S. Bartlett, The Statistical Analysis of Spatial Pattern. London, England: Chapman and Hall, 1976.
53. R. L. Kashyap and R. Chellappa “Estimation and choice of neighbors in spatial interaction models of images,” IEEE Trans. Informat. Theory, vol. IT-29, no. 1, pp. 60–72, Jan. 1983.