References

  1. [1]
    R. J. Schalkoff, Digital image processing and computer vision. Singapore: John Wiley and Sons, 1989.Google Scholar
  2. [2]
    Y. Ohta, Knowledge Based Interpretation of Outdoor Natural Color Scenes. Boston: Pitman, 1985.Google Scholar
  3. [3]
    V. P. Kumar and U. B. Desai, “Image interpretation using a Bayesian network,” Tech. Rep. SPANN.96.3, SPANN Lab, Dept. of Elect. Engrg., Indian Institute of Technology-Bombay, May 1996.Google Scholar
  4. [4]
    A. Grasselli, Automatic interpretation and classification of images. New York Academic Press, 1969.Google Scholar
  5. [5]
    H. Andrews, Automatic Interpretation and Classification of Images by Use of the Fourier Domain. New York Academic Press, 1969.Google Scholar
  6. [6]
    R. Narasimham, On the Description, Generation, and Recognition of Classes of Pictures. New York Academic Press, 1969.Google Scholar
  7. [7]
    I. Hofmann, H. Niemann, and G. Sagerer, “Model based interpretation of image sequences from the heart,” in Proceedings of an international workshop held in Amsterdam, Holland, 1985.Google Scholar
  8. [8]
    C. Sagerer, “Automatic interpretation of medical image sequences,” Pattern Recognition Letters, pp. 87–102, 1988.Google Scholar
  9. [9]
    N. Karssemeijer, Interpretation of medical images by model guided analysis. PhD thesis, Katholieke Universiteit Leuven, 1989.Google Scholar
  10. [10]
    R. Baldock, “Trainable models for the interpretation of biomedical images,” Image and Vision Computing, pp. 444–450, 1992.Google Scholar
  11. [11]
    T. Cootes, A. Hill, C. Taylor, and J. Haslam, “Use of active shape models for locating structure in medical images,” Image and Vision Computing, pp. 355–365, 1994.Google Scholar
  12. [12]
    J. Desachy, “A knowledge-based system for satellite image interpretation,” in Proceedings 11th IAPR International Conference on Pattern Recognition, pp. 198–200, 1992.Google Scholar
  13. [13]
    M. Nagao and T. Matsuyama, A Structural Analysis of Complex Aerial Photographs. New York Plenum, 1980.Google Scholar
  14. [14]
    J. McKendrick and M. Lybanon, “Knowledge-based interpretation aids to the navy oceanographic image analyst,” in Proceedings: Image Understanding Workshop, pp. 61–63, 1985.Google Scholar
  15. [15]
    D. M. Jr. and W. Harvey, “Automating knowledge acquisition for aerial image interpretation:” in Image Understanding Workshop, 1987.Google Scholar
  16. [16]
    T. Silberberg, “Multiresolution aerial image interpretation,” in Proceedings Image Understanding Workshop, pp. 505–511, 1988.Google Scholar
  17. [17]
    D. Kuan, H. Shariat, K. Dutta, and P. Ransil, “A constraint-based system for interpretation of aerial imagery,” in Second International Conference on Computer Vision, 1988.Google Scholar
  18. [18]
    D. M. Jr., W. Harvey, and L. Wixson, “Automating knowledge acquisition for aerial image interpretation:” CVGIP: Image Understanding, pp. 37–81, 1989.Google Scholar
  19. [19]
    P. Garnesson, G. Giraudon, and P. Montesinos. “An image analysis system, application for aerial imagery interpretation,” in Tenth International Conference on Pattern Recognition, 1990.Google Scholar
  20. [20]
    V. Venkateswar and R. Chellappa, “A framework for interpretation of aerial images,” in Tenth International Conference on Pattern Recognition, 1990.Google Scholar
  21. [21]
    K. Schutte, Knowledge Based Recognition of Man-Made Objects. PhD thesis, University of Twente, P.O. Box 217 7500 AE Enschede The Netherlands, February 1994.Google Scholar
  22. [22]
    B. Draper, R. Collins, J. Brolio, J. Griffith, A. Hanson, and E. Riseman, “Tools and experiments in the knowledge-directed interpretation of road scenes,” in Image Understanding Workshop, 1987.Google Scholar
  23. [23]
    Y. Ozaki, K. Sato, and S. Inokuchi, “Rule-driven processing and recognition from range image,” in Intel: Conf. on Pattern Recog., pp. 804–807, 1988.Google Scholar
  24. [24]
    D. Chelberg, “Uncertainty in interpretation of range imagery,” in Third International Conference on Computer Vision, 1990.Google Scholar
  25. [25]
    J. Aggarwal and N. Nandhakumar, Multisensor Fusion for Automatic Scene Interpretation. Ramesh C. Jain and Anil K. Jain, Analysis and Interpretation of Range Images: Springer-Verlag, 1990.Google Scholar
  26. [26]
    R. C. Jain and A. K. Jain, Analysis and Interpretation of Range Images. Springer-Verlag, 1990.Google Scholar
  27. [27]
    T. Strat and M. Fischler, “A context-based recognition system for natural scenes and complex domains,” in Image Understanding Workshop, pp. 456–472, 1990.Google Scholar
  28. [28]
    M. Hild and Y. Shirai, “Interpretation of natural scenes using multi-parameter default models and qualitative constraints,” in International Conference on Computer Vision, pp. 497–501, 1993.Google Scholar
  29. [29]
    T. Silberberg, “Infrared image interpretation using spatial and temporal knowledge,” in Workshop on Computer Vision, pp. 264–267, 1987.Google Scholar
  30. [30]
    N. Nandhakumar and J. Aggarwal, “Integrated analysis of thermal and visua images for scene interpretation,” IEEE Trans. on Patt. Anal. and Mach. Intell., pp. 469–431, 1988.Google Scholar
  31. [31]
    A. Taylor, A. Gross, D. Hogg, and D. Mason, “Knowledge-based interpretation of remotely sensed images,” IVC, pp. 67–83, 1986.Google Scholar
  32. [32]
    V. Clement, G. Giraudon, and S. Houzelle, “Interpretation of remotely sensed images in a context of multisensor fusion,” in Second European Conference on Compute Vision, 1992.Google Scholar
  33. [33]
    Z. Zhang and M. Simaan, “A rule-based interpretation system for segmentation of seismic images,” Pattern Recognition, pp. 45–53, 1987.Google Scholar
  34. [34]
    A. Heller, D. LaRocque, and J. Mundy, “The interpretation of synthetic aperture radar images using projective invariants and deformable templates,” in DARPA Image Understanding Workshop, pp. 831–837, 1992.Google Scholar
  35. [35]
    C.-C. Chu and J. Aggarwal, “The interpretation of laser radar images by a knowledge-based system,” Machine Vision and Applications, pp. 145–163, 1991.Google Scholar
  36. [36]
    M. Kurtz, P. Mussio, and P. Ossorio, “A cognitive system for astronomical image interpretation,” Pattern Recognition Letters, pp. 507–515, 1990.Google Scholar
  37. [37]
    S. Towers and R. Baldock, “Application of a knowledge-based system to the interpretation of ultrasound images,” in Ninth International Conference on Pattern Recognition, 1988.Google Scholar
  38. [38]
    V. Roberto, A. Peron, and P. Fumis, “Low-level processing techniques in geophysical image interpretation,” Pattern Recognition Letters, pp. 111–122, 1989.Google Scholar
  39. [39]
    K. Sugimoto, M. Takahashi, and F Tomita, “Scene interpretation based on boundary representations of stereo images,” in Ninth International Conference on Pattern Recognition, 1988.Google Scholar
  40. [40]
    T. Pridmore, J. Mayhew, and J. Frisby, “Exploiting image-plane data in the interpretation of edge-based binocular disparity,” Computer Vision, Graphics, and Image Processing, pp. 1–25, 1990.Google Scholar
  41. [41]
    Y. L. Guilloux, “Automatic computation of motion in an image sequence, interest for interpretation,” Signal Processing, pp. 377-, 1985.Google Scholar
  42. [42]
    A. Milano, F Perotti, S. Serpico, and G. Vemazza, “A system for the interpretation of 3-d moving scenes from 2-d image sequences,” International Journal of Pattern Recognition and Artificial Intell., pp. 765–796, 1991.Google Scholar
  43. [43]
    S. Tsuji, “Continuous image interpretation by a moving viewer,” in Ninth International Conference on Pattern Recognition, pp. 514–519, 1988.Google Scholar
  44. [44]
    T. Binford, “Survey of model based image analysis systems,” Int. J. Roborics Res., pp. 587–633, 1982.Google Scholar
  45. [45]
    C. Smyrmiotis and K. Dutta, “A knowledge-based system for recognizing man-made objects in aerial images,” in Proc. Comp. Vis. and Patt. Recog., pp. 111–117, 1988.Google Scholar
  46. [46]
    D. Ballard, C. Brown, and J. Feldman, “An approach to knowledge-directed scene analysis:’ in CVS, pp. 271–281, 19xx.Google Scholar
  47. [47]
    A. Mitiche, A. Mansouri, and C. Meubus, “A knowledge based image interpretation system,” in Ninth International Conference on Pattern Recognition, 1988.Google Scholar
  48. [48]
    C.-C. Chu and J. Aggarwal, “Image interpretation using multiple sensing modalities,” IEEE Trans. on Patt. Anal. and Mach. Intell., pp. 840–847, 1992.Google Scholar
  49. [49]
    V. Roberto, “Knowledge-based understanding of signals: An introduction,” Signal Processing, pp. 29–56, 1993.Google Scholar
  50. [50]
    P. Puliti and G. Tascini, “Knowledge-based approach to image interpretation,” Image and Vision Computing, pp. 122–128, 1993.Google Scholar
  51. [51]
    J. Smolle, R. Hofmann-Wellenhof, and H. Kerl, “Pattern interpretation by cellular automata (pica)-evaluation of tumour cell adhesion in human melanomas,” Analytical Cellular Pathology, pp. 91–106, 1994.Google Scholar
  52. [52]
    R. Evangelista and 0. Salvetti, “A morphometric and densitometric approach to image interpretation,” Pattern Recognition and Image Analysis, pp. 305–310, 1993.Google Scholar
  53. [53]
    W. Dickson, “Feature grouping in a hierarchical probabilistic network,” Image and Vision Computing, pp. 51–57, 1991.Google Scholar
  54. [54]
    F. V. Jensen, H. I. Christensen, and J. Nielsen, “Bayesian methods for interpretation and control in multi-agent vision systems,” Applications of Artificial Intelligence X: Machine Vision and Robotics, SPIE Proceedings Series, vol. 1708, 1992.Google Scholar
  55. [55]
    W. B. Mann and T. O. Binford, “An example of 3-D interpretation of images using Bayesian networks,” in Proceedings DARPA Image Understanding Workshop,, 1992.Google Scholar
  56. [56]
    V. P. Kumar and U. B. Desai, “Image interpretation using Bayesian networks,” IEEE Trans. on Pattern Anal. and Machine Intell., pp. 74–77, 1996.Google Scholar
  57. [57]
    W. Wilhelmi, “Image interpretation by algebraic topology,” Pattern Recognition and Image Analysis, pp. 126–134, 1992.Google Scholar
  58. [58]
    J. A. Modestino and J. Zhang, “A Markov random field model based approach to image interpretation,” IEEE Trans. on Patt. Anal. and Mach. Intell., pp. 606–615, 1992.Google Scholar
  59. [59]
    I. Y. Kim and H. S. Yang, “Efficient image labeling based on Markov random field and error backpropagation network,” Pattern Recog., pp. 1695–1707, 1993.Google Scholar
  60. [60]
    J. M. Tenenbaum and H. G. Barrow, “Experiments in interpretation guided segmentation,” Artificial Intelligence, pp. 241–274, 1977.Google Scholar
  61. [61]
    R. Bajcsy, F Solina, and A. Gupta, Segmentation versus Object Representation-Are They Separable? Springer-Verlag, 1990.Google Scholar
  62. [62]
    M. Sonka, S. K. Tadikonda, and S. M. Collins, “Genetic algorithms in hypothesize-and-verify image interpretation,” Proc. SPIE-Sensor Fusion VI, pp. 236–247,1993.Google Scholar
  63. [63]
    R. A. Schowengerdt, Techniques for Image Processing and Classification in Remote Sensing. New York: Academic Press, 1983.Google Scholar
  64. [64]
    B. Draper, J. Brolio, R. Collins, A. Hanson, and E. Riseman, “Image interpretation by distributed cooperative processes,” in Proc. Comp. Vis. and Patt. Recog., 1988.Google Scholar
  65. [65]
    K. S. Kumar and U. B. Desai, “Joint segmentation and image interpretation,” Tech. Rep. SPANN.96.2, SPANN Lab, Dept. of Elect. Engrg., Indian Institute of Technology-Bombay, May 1996.Google Scholar
  66. [66]
    J. Pearl, “Fusion, propagation and structuring in belief networks,” Artificial Intelligence, pp. 241–288, 1986.Google Scholar
  67. [67]
    J. Pearl, “Evidential reasoning using stochastic simulation of causal models:’ Artificial Intelligence, pp. 245–257, 1987.Google Scholar
  68. [68]
    J. Pearl, Probabilistic Reasoning in Intelligent Systems. New York: Morgan Kaufmann, 1988.Google Scholar
  69. [69]
    R. E. Neapolitan, Probabilistic Reasoning in Expert Systems. New York: John Wiley, 1988.Google Scholar
  70. [70]
    G. Shafer, A Mathemetical Theory of Evidence. Princeton, New Jersey: Princeton University Press, 1976.Google Scholar
  71. [71]
    E. J. Horovitz, D. E. Heckerman, and C. P. Langlotz, “A framework for comparing alternative formalism for plausible reasoning,” in Proc. of the Fifth National. on AI, (Philadelphia, Pennsylvania), 1986.Google Scholar
  72. [72]
    I. Y. Kim and H. S. Yang, “An integrated approach for scene understanding based on Markov random field,” Pattern Recog., pp. 1887–1897, 1995.Google Scholar
  73. [73]
    I. Y. Kim and H. S. Yang, “An integration scheme for image segmentation and labeling based on Markov random fields,” IEEE Trans. on Patt. Anal. and Mach. Intell., pp. 69–73, 1996.Google Scholar
  74. [74]
    K. S. Kumar and U. B. Desai, “Joint segmentation and image interpretation,” Pattern Recognition, pp. 557–589, April 1999.Google Scholar
  75. [75]
    D. Marr, Vision. San Francisco: W. H. Freeman and Co., 1982.Google Scholar
  76. [76]
    B. K. P. Horn, Robot Vision. Cambridge: MIT Press, 1986.Google Scholar
  77. [77]
    D. Mumford and J. Shah, “Optimal approximations by piecewise smooth functions and variational problems,” Communication of Pure and Applied Variational Problems, vol. XLII,no. 5, pp. 577–685, 1988.MathSciNetGoogle Scholar
  78. [78]
    J. Besag, “Spatial interaction and the statistical analysis of lattice systems,” J. Royal Statistical Society, pp. 192–236, 1974.Google Scholar
  79. [79]
    S. Geman and D. Geman, “Stochastic relaxation, Gibbs distribution, and Bayesian restoration of images,” IEEE Trans. on Patt. Anal. and Mach. Intell., pp. 721–741, 1984.Google Scholar
  80. [80]
    S. Kirkpatrick, C. S. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, pp. 671–680, 1983.Google Scholar
  81. [81]
    B. Hajek, “Cooling schedules for optimal annealing,” Mathematics of Operations Research, vol. 134, pp. 311–329, 1989.MathSciNetGoogle Scholar
  82. [82]
    E. Aarts and J. Korst, Simulated annealing and Boltzmann machines. John Wiley, 1989.Google Scholar
  83. [83]
    G. E. Hinton and T. J. Sejnowski, “Learning and relearning in Boltzmann machines,” in Parallel and Distributed Processing (D. E. Rumelhart, L. McClelland, and the PDP Research Group, eds.), MIT Press, 1988.Google Scholar
  84. [84]
    C. Koch, J. Marroquin, and A. Yuille, “Analog neuronal networks in early vision,” Proc. National Academic Sciences, pp. 4263–4267, 1986.Google Scholar
  85. [85]
    A. L. Yuille, “Energy functions for early vision and analog networks,” Biological Cybernetics, vol. 61, pp. 115–123, 1989.Google Scholar
  86. [86]
    J. Zerubia and R. Chellappa, “Mean Field Annealing for edge detection and image restoration,” in European Conference on Computer Vision, 1990.Google Scholar
  87. [87]
    D. Geiger and E Girosi, “Parallel and deterministic algorithms from MRF’s: Surface reconstruction,” IEEE Tran. on Pattern Analysis and Machine Intelligence, vol. 13, pp. 401–412, 1991.Google Scholar
  88. [88]
    M. R. Bhatt and U. B. Desai, “Robust image restoration algorithm using Markov random field model,” Graphical Models and Image Proc., vol. 56, pp. 61–74, January 1994.Google Scholar
  89. [89]
    E. king, «Beitag sur theorie des ferromagnetismus,» Zeit. fur Physik, vol. 31, pp. 253–258, 1925.Google Scholar
  90. [90]
    J. Marroquin, S. Mitter, and T. Poggio, “Probabilistic solution of ill-posed problems in computational vision,” ASAJ, vol. 82, pp. 76–89, March 1987.Google Scholar
  91. [91]
    S. Geman and C. Graffigne, “Markov random fields image models and their applications to computer vision,” in Proc. Int. Congr. Math., 1987.Google Scholar
  92. [92]
    F. J. Solis and J. B. Wets, “Minimization by random search techniques,” Math. of Operation Research, vol. 6, pp. 19–30, 1991.MathSciNetGoogle Scholar
  93. [93]
    N. Metropolis, A. Rosenbluth, M. Rosenbluth, and E. Teller, “Equation of state calculations by fast computing machines,” J. of Chem. Physics. vol. 21, pp. 1087–1092, 1953.Google Scholar
  94. [94]
    S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation:’ IEEE Trans. on Pattern Anal. and Machine Intell., pp. 674–693, 1989.Google Scholar
  95. [95]
    P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Tran on Comm., pp. 532–540, 1983.Google Scholar
  96. [96]
    I. Daubechies, Ten Lectures on Wavelets. Philadelphia, Pennsylvania: SIAM, 1992.Google Scholar
  97. [97]
    D. Gabor, “Theory of communication,” Journal of I.E.E., vol. 93, pp. 429–441, 1946.Google Scholar
  98. [98]
    Y. Meyer, Wavelets. Berlin,: Springer Verlag, 1989.Google Scholar
  99. [99]
    P. P. Vaidyanathan, Multirate systems and filter banks. Englewoods Cliff, New Jersey: Prentice Hall, 1993.Google Scholar
  100. [100]
    J. M. Jolion and A. Rosenfeld, A pyramid framework for early vision. The Netherlands: Kluwer Academic Publishers, 1994.Google Scholar
  101. [101]
    S. K. Kopparapu, U. B. Desai, and P. I. Corke, “Behaviour of image degradation model in multiresolution,” Signal Processing, vol. 80, pp. 2407–2420, 2000.CrossRefGoogle Scholar
  102. [102]
    D. Marr and T. Poggio, “A computational theory of human stereo vision,” in Procedings Royal Society London, 1979.Google Scholar
  103. [103]
    B. Kosko, Neural Networks and Fuzzy Systems. India: Prentice Hall, 1992.Google Scholar
  104. [104]
    L. Davis, Genetic Algorithms and Simulated Annealing. London: Pitman Publishing, 1987.Google Scholar
  105. [105]
    R. Prasannappa, L. Davis, and V. S. S. Hwang, “A knowledge-based vision system for aerial image understanding,” CS-TR, p. 1785, 1987.Google Scholar
  106. [106]
    D. M. McKeown, W. A. Harvey, and J. McDermott, “Rule-based interpretation of aerial imagery,” IEEE Tran. on Pattern Analysis and Machine Intelligence, vol. 7, pp. 570–585, 1985.Google Scholar
  107. [107]
    J. Pearl, “Fusion, propagation and structuring in belief networks,” Artificial Intelligence, vol.. 29, pp. 241–288, 1986.CrossRefMATHMathSciNetGoogle Scholar
  108. [108]
    R. E. Neapolitan, Probabilistic Reasoning in Expert Systems. John Wiley, 1990.Google Scholar
  109. [109]
    S. L. Lauritzen and D. J. Spiegelhalter, “Local computation with probabilities in graphical structures and their applications to expert systems,” Journal of the Royal Statistical Society series B, vol. 50, 1988.Google Scholar
  110. [110]
    J. Pearl, “Evidential reasoning using stochastic simulation of causal models,” Artificial Intelligence, vol. 32, pp. 245–257, 1987.CrossRefMATHMathSciNetGoogle Scholar
  111. [111]
    G. F, Cooper, “The computational complexity of probabilistic inference using Bayesian belief networks,” Artificial Intelligence, vol. 42, pp. 393–405, 1990.CrossRefMATHMathSciNetGoogle Scholar
  112. [112]
    R. M. Chavez and G. F Cooper, “A randomized approximation algorithm for probabilistic inference on the Bayesian belief networks,” Networks, vol. 20, pp. 661–685, 1990.MathSciNetGoogle Scholar
  113. [113]
    P. Dagum and M. Luby, “Approximating probabilistic inference in Bayesian belief networks is NP-hard,” Artificial Intelligence, vol. 60, pp. 141–153, 1993.CrossRefMathSciNetGoogle Scholar
  114. [114]
    K. S. Kumar, Modular Integration for Low-level and High-level Vision Problems in a Multiresolution Framework. PhD thesis, Indian Institute of Technology-Bombay, 1997.Google Scholar
  115. [115]
    K. S. Kumar and U. B. Desai, “Joint segmentation and image interpretation,” Tech. Rep. SPANN.96.2, Indian Institute of Technology-Bombay, May 1996.Google Scholar
  116. [116]
    S. Peleg, 0. Federbusch, and R. Hummel, “Custom made pyramids,” in Parallel Computer vision (L. Uhr, ed.), pp. 125–146, Academic Press, 1987.Google Scholar
  117. [117]
    N. Kamath, K. S. Kumar, U. B. Desai, and R. Duggad, “Joint segmentation and image interpretation using HMM,” in Proceedings of the International Conference on Pattern Recognition, (Brisbane, Austalia), August 1998.Google Scholar
  118. [118]
    Y. Linde, A. Buzo, and R. M. Gray, “An algorithm for vector quantization design,” IEEE Trans. Communictions, vol. 28, pp. 84–95, 1980.Google Scholar
  119. [119]
    J. C. Russ, The image processing handbook. Boca Raton, Florida: CRC Press, 1994.Google Scholar
  120. [120]
    E. R. Davies, Machine vision: theory, algorithms, practicalities. London: Academic Press, 1990.Google Scholar

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