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Image understanding

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Image Processing, Analysis and Machine Vision

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Abstract

Image understanding requires mutual interaction of processing steps. The building blocks necessary for image understanding have been presented in earlier chapters — now an internal image model must be built that represents the machine vision syste’s concept about the processed image of the world.

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References

  1. M E Algorri, D R Haynor, and Y Kim: Contextual classification of multiple anatomical tissues in tomographic images. In Proceedings of the Annual International Conference IEEE EMBS, Vol.13, 1991, Orlando, Fl, pages 106–107, IEEE, Piscataway, NJ, 1991.

    Google Scholar 

  2. A P H Ambler: A versatile system for computer controlled assembly. Artificial Intelligence, 6 (2): 129–156, 1975.

    Article  MathSciNet  MATH  Google Scholar 

  3. A Amini, S Tehrani, and T Weymouth: Using dynamic programming for minimizing the energy of active contours in the presence of hard constraints. In Proceedings, Second International Conference on Computer Vision, Tampa, Fl, pages 95–99, IEEE, Piscataway, NJ, 1988.

    Google Scholar 

  4. A Amini, T Weymouth, and R Jain: Using dynamic programming for solving variational problems in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (9): 855–867, 1990.

    Article  Google Scholar 

  5. R Bajcsy, and D A Rosenthal: Visual and conceptual focus of attention. In S Tanimoto and A Klinger, editors, Structured Computer Vision, pages 133–154. Academic Press, New York, 1980.

    Google Scholar 

  6. D H Ballard, and C M Brown: Computer Vision. Prentice-Hall, Englewood Cliffs, NJ, 1982.

    Google Scholar 

  7. H G Barrow, and I M Tenenbaum: MSYS: A system for reasoning about scenes. Technical Report Tech. Note 121, Stanford Research Institute, Menlo Park, Ca, 1976.

    Google Scholar 

  8. S Basu: Image segmentation by semantic method. Pattern Recognition, 20 (5): 497–511, 1987.

    Article  Google Scholar 

  9. M O Berger, and R Mohr: Towards autonomy in active contour models. In Proceedings, 10th International Conference on Pattern Recognition, Atlantic City, NJ, pages 847–851, IEEE, Piscataway, NJ, 1990.

    Google Scholar 

  10. M Berthod, and O D Faugeras: Using context in the global recognition of a set of objects: an optimisation approach. In Proceedings of the 8th World Computing Congress (IFIP), Tokyo, Japan, pages 695–698, 1980.

    Google Scholar 

  11. S Bhandarker, and M Suk: Computer vision as a coupled system. In Applications of Artificial Intelligence VIII, Orlando, Fl, pages 43–54, SPIE, Bellingham, Wa, 1990.

    Google Scholar 

  12. A Blake: A convergent edge relaxation algorithm. Technical Report MIP-R-135, Machine Intelligence Unit, University of Edinbourgh, 1982.

    Google Scholar 

  13. R A Brooks, R Greiner, and T O Binford: The ACRONYM model-based vision system. In Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI-6, Tokyo, pages 105113, 1979.

    Google Scholar 

  14. D Cabello, A Delgado, M J Carreira, J Mira, R Moreno: Diaz, J. A. Munoz, and S. Candela. On knowledge-based medical image understanding. Cybernetics and Systems, 21 (2–3): 277–289, 1990.

    Article  Google Scholar 

  15. G A Cohen: Optimization of Radiologic Imaging Through Anatomic Classification: An Application to Magnetic Resonance Imaging. PhD thesis, University of Iowa, 1991.

    Google Scholar 

  16. L D Cohen: On active contour models and balloons. CVGIP — Image Understanding, 53 (2): 211–218, 1991.

    Article  MATH  Google Scholar 

  17. L D Cohen and I Cohen. Deformable models for 3D medical images using finite elements, and balloons. In Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, Champaign, Il, pages 592–598, IEEE, Los Alamitos, Ca, 1992.

    Google Scholar 

  18. S M Collins, C J Wilbricht, S R Fleagle, S. Tadikonda, and M D Winniford: An automated method for simultaneous detection of left and right coronary borders. In Computers in Cardiology 1990, Chicago, Il, page 7, IEEE, Los Alamitos, Ca, 1991.

    Google Scholar 

  19. P A Devijver, and J Kittler: Pattern Recognition Theory and Applications. Springer Verlag, Berlin-New York-Tokyo, 1986.

    Google Scholar 

  20. P M Dew, R A Earnshaw, and T R Heywood, editors. Parallel Processing for Computer Vision and Display. Addison-Wesley, Reading, Ma, 1989.

    MATH  Google Scholar 

  21. T Elfving and J O Eklundh. Some properties of stochastic labeling procedures. Computer Graphics and Image Processing, 20: 158–170, 1982.

    Article  MATH  Google Scholar 

  22. J A Feldman, and Y Yakimovsky: Decision theory and artificial intelligence: A semantic—based region analyzer. Artificial Intelligence, 5: 349–371, 1974.

    Article  MATH  Google Scholar 

  23. S E Franklin. Topographic context of satellite spectral response. Computers 81 Geosciences, 16 (7): 1003–1010, 1990.

    Article  MathSciNet  Google Scholar 

  24. P W Fung, G Grebbin, and Y Attikiouzel: Contextual classification and segmentation of textured images. In Proceedings of the 1990 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 90, Albuquerque, NM, pages 2329–2332, IEEE, Piscataway, NJ, 1990.

    Google Scholar 

  25. S Geman, and D Geman: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6 (6): 721–741, 1984.

    Article  MATH  Google Scholar 

  26. G Gerig, J Martin, R Kikinis, 0 Kubler, M Shenton, and F A Jolesz: Unsupervised tissue type segmentation of 3D dual-echo MR head data. Image and Vision Computing, 10 (6): 349–360, 1992.

    Article  Google Scholar 

  27. J Ghosh and C G Harrison, editors: Parallel Architectures for Image Processing, Santa Clara, Ca, Bellingham, Wa, 1990. SPIE.

    Google Scholar 

  28. A F Gonzalez, and S S Lopez: Classification of satellite images using contextual classifiers. Digest - International Geoscience and Remote Sensing Symposium (IGARSS), 2: 645–648, 1989.

    Google Scholar 

  29. W E L Grimson and T Lozano-Perez. Localizing overlapping parts by searching the interpretation tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9 (4): 469–482, 1987.

    Article  Google Scholar 

  30. E R Hancock and J Kittler. Discrete relaxation. Pattern Recognition, 23 (7): 711–733, 1990.

    Article  Google Scholar 

  31. E R Hancock and J Kittler. Edge-labeling using dictionary-based relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (2): 165–181, 1990.

    Article  Google Scholar 

  32. A R Hanson, and E M Riseman: VISIONS — a computer system for interpreting scenes. In A R Hanson and E M Rise-man, editors, Computer Vision Systems, pages 303–333. Academic Press, New York, 1978.

    Google Scholar 

  33. R M Haralick, M C Zhang, and R W Ehrich: Dynamic programming approach for context classification using the Markov random field. In 9th International Conference on Pattern Recognition, Rome, Italy, pages 1169–1181, IEEE, New York, 1988.

    Google Scholar 

  34. R A Hummel, and S W Zucker: On the foundation of relaxation labeling proceses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5 (3): 259–288, 1983.

    Google Scholar 

  35. M E Hyche, N F Ezquerra, and R Mullick. Spatiotemporal detection of arterial structure using active contours. In Proceedngs of Visualization in Biomedical Computing ‘82 Proceedings, Chapel Hill, NC, pages 52–62, 1992.

    Google Scholar 

  36. J Illingworth, and J Kittler: Optimisation algorithms in probabilistic relaxation labelling. In Pattern Recognition Theory and Applications, pages 109–117. Springer Verlag, Berlin-New York-Tokyo, 1987.

    Chapter  Google Scholar 

  37. M Kamada, K Toraichi, R Mori, K Yamamoto, and H. Yamada: Parallel architecture for relaxation operations. Pattern Recognition, 21 (2): 175–181, 1988.

    Article  Google Scholar 

  38. T Kanade, and K Ikeuchi: Special issue on physical modeling in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13: 609–742, 1991.

    Google Scholar 

  39. P Karaolani, G D Sullivan, and K D Baker. Active contours using finite elements to control local scale. In D C Hogg and R D Boyle, editors, Proceedings of the 1992 British Machine Vision Conference, Leeds, UK, pages 472–480. Springer Verlag, 1992.

    Google Scholar 

  40. S Kasif: On the parallel complexity of discrete relaxation in constraint satisfaction networks. Artificial Intelligence, 45 (3): 275–286, 1990.

    Article  MathSciNet  MATH  Google Scholar 

  41. Kass et al. 87a] M Kass, A Witkin, and D Terzopoulos: Snakes: Active contour models. International Journal of Computer Vision,1(4):133144, 1987.

    Google Scholar 

  42. M Kass, A Witkin, and D Terzopoulos: Snakes: Active contour models. In Proceedings, First International Conference on Computer Vision, London, England, pages 259–268, IEEE, Piscataway, NJ, 1987.

    Google Scholar 

  43. J Kittler: Relaxation labelling. In Pattern Recognition Theory and Applications, pages 99–108. Springer Verlag, Berlin-New York-Tokyo, 1987.

    Chapter  Google Scholar 

  44. J Kittler, and J Foglein: Contextual classification of multispectral pixel data. Image and Vision Computing, 2 (1): 13–29, 1984.

    Article  Google Scholar 

  45. J Kittler, and J Foglein: Contextual decision rules for objects in lattice configuration. In Proceedings of 7th International Conference on Pattern Recognition, Montreal, Canada, pages 270–272. IEEE, 1984.

    Google Scholar 

  46. J Kittler, and J Foglein: On compatibility and support functions in probabilistic relaxation. Computer Vision, Graphics, and Image Processing, 34: 257–267, 1986.

    Google Scholar 

  47. J Kittler, and E R Hancock: Combining evidence in probabilistic relaxation. International Journal on Pattern Recognition and Artificial Intelligence, 3: 29–52, 1989.

    Article  Google Scholar 

  48. J Kittler, and J Illingworth: Relaxation labelling algorithms — a review. Image and Vision Computing, 3 (4): 206–216, 1985.

    Article  Google Scholar 

  49. J Kittler, and D Pairman: Contextual pattern recognition applied to cloud detection and identification. IEEE Transactions on Geoscience and Remote Sensing, 23 (6): 855–863, 1985.

    Article  Google Scholar 

  50. D Kuan, H Shariat, and K Dutta. Constraint-based image understanding system for aerial imagery interpretation. In Proceedings of the Annual AI Systems in Government Conference, Washington, DC, pages 141–147, 1989.

    Google Scholar 

  51. D Lee, A Papageorgiou, and G W Wasilkowski: Computing optical flow. In Proceedings, Workshop on Visual Motion, pages 99106, IEEE, Irvine, Ca, 1989.

    Google Scholar 

  52. V R Lesser, R D Fennell, L D Erman, and D R Reddy: Organisation of the HEARSAY II speech understanding system. IEEE Transactions on Acoustics, Speech and Signal Processing, 23 (1): 1124, 1975.

    Article  Google Scholar 

  53. M D Levine. A knowledge based computer vision system. In A R Hanson and E M Riseman, editors, Computer Vision Systems, pages 335–352. Academic Press, New York, 1978.

    Google Scholar 

  54. Z Li, and L Uhr: Pyramid vision using key features to integrate image-driven bottom-up and model-driven top-down processes. IEEE Transactions on Systems, Man and Cybernetics, 17 (2): 250–263, 1987.

    Article  Google Scholar 

  55. Y Liow, and T Pavlidis: Enhancements of the splitand-merge algorithm for image segmentation. In 1988 IEEE International Conference on Robotics and Automation, Philadelphia, Pa, pages 1567–1572, Computer Society Press, Washington, DC, 1988.

    Google Scholar 

  56. V Marik, 0 Stepankova, and R Trappl, editors: Advanced Topics in Artificial Intelligence, LNAI No. 617. Springer Verlag, Heidelberg, 1992.

    Google Scholar 

  57. D Marr: Vision - A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman and Co., San Francisco, 1982.

    Google Scholar 

  58. T McInerney, and D Terzopoulos: A finite element based deformable model for 3D biomedical image segmentation. In Proceedings SPIE, Vol. 1905, Biomedical Image Processing and Biomedical Visualization, San Jose, Ca, SPIE, Bellingham, Wa, 1993.

    Google Scholar 

  59. D Metaxas, and D Terzopoulos: Constrained deformable superquadrics and nonrigid motion tracking. In Proceedings of the Computer Vision and Pattern Recognition Conference CVPR-91, Lahaina, Hi, pages 337–343, 1991.

    Google Scholar 

  60. R S Michalski, J G Carbonell, and T M Mitchell: Machine Learning I, II. Morgan Kaufmann Publishers, Los Altos, Ca, 1983.

    Google Scholar 

  61. B M Millin, and L M Ni: A reliable parallel algorithm for relaxation labeling. In P M Dew, R A Earnshaw, and T R Heywood, editors, Parallel Processing for Computer Vision and Display, pages 190–207. Addison-Wesley, Reading, Ma, 1989.

    Google Scholar 

  62. Mohn et al. 87] E Mohn, N L Hjort, and G 0 Storvik: Simulation study of some contextual classification methods for remotely sensed data. IEEE Transactions on Geoscience and Remote Sensing,25(6):796804, 1987.

    Google Scholar 

  63. L Moller-Jensen: Knowledge-based classification of an urban area using texture and context information in LandsatTM imagery. Photogrammetric Engineering and Remote Sensing, 56 (6): 899–904, 1990.

    Google Scholar 

  64. J A Mulder: Discrimination vision. Computer Vision, Graphics, and Image Processing, 43: 313–336, 1988.

    Google Scholar 

  65. M Nagao and T Matsuyama. A Structural Analysis of Complex Aerial Photographs. Plenum Press, New York, 1980.

    Book  Google Scholar 

  66. Niemann 90] H Niemann: Pattern Analysis and Understanding. Springer Verlag, Berlin-New York-Tokyo, 2nd edition, 1990.

    Google Scholar 

  67. N J Nilsson: Principles of Artificial Intelligence. Springer Verlag, Berlin, 1982.

    Book  MATH  Google Scholar 

  68. P Parent and S W Zucker. Radial projection: an efficient update rule for relaxation labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11 (8): 886–889, 1989.

    Article  Google Scholar 

  69. J Parkkinen, G Cohen, M Sonka, J C Ehrhardt, and N. Andreasen: Some problems of brain image analysis. In Proceedings of Biosignal ‘80, Brno, Czechoslovakia. Czech Technical Society, 1990.

    Google Scholar 

  70. J Parkkinen, G Cohen, M Sonka, and N Andreasen: Segmentation of MR brain images. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Volume 19, Orlando, Fl, pages 71–72, IEEE, Piscataway, NJ, 1991.

    Google Scholar 

  71. T Pavlidis, and Y Liow: Integrating region growing and edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (3): 225–233, 1990.

    Article  Google Scholar 

  72. V K Prasanna Kumar: Parallel Architectures and Algorithms for Image Understanding. Academic Press, Boston, Ma, 1991.

    Google Scholar 

  73. A R Rao, and R Jain: Knowledge representation and control in computer vision systems. IEEE Expert, 3 (1): 64–79, 1988.

    Article  Google Scholar 

  74. H Reichgelt: Knowledge Representation: An AI Perspective. Ablex Publishing Corporation, Norwood, NJ, 1991.

    Google Scholar 

  75. V Roberto, L Gargiulo, A Peron, and C Chiaruttini: A knowledge-based system for geophysical interpretation. In Proceedings of the 1990 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 90, Albuquerque, NM, pages 1945–1948, IEEE, Piscataway, NJ, 1990.

    Google Scholar 

  76. A Rosenfeld. Picture Languages - Formal Models for Picture Recognition. Academic Press, New York, 1979.

    MATH  Google Scholar 

  77. A Rosenfeld, R A Hummel, and S W Zucker: Scene labelling by relaxation operations. IEEE Transactions on Systems, Man and Cybernetics, 6: 420–433, 1976.

    Article  MathSciNet  MATH  Google Scholar 

  78. G L Simons: Introducing Artificial Intelligence. NCC Publications, Manchester, 1984.

    Google Scholar 

  79. M Sonka, C J Wilbricht, M D Winniford, and S M Collins Simultaneous detection of left and right coronary borders: A robust approach to automated angiographic analysis. Circulation (, 86 (4): I121, 1992.

    Google Scholar 

  80. M Sonka, M D Winniford, and S M. Collins: Reduction of failure rates in automated analysis of difficult images: Improved simultaneous detection of left and right coronary borders. In Computers in Cardiology, Durham, NC, 1992, pages 111–114, IEEE, Los Alamitos, CA, 1992.

    Google Scholar 

  81. M Sonka, C J Wilbricht, S R Fleagle, S K Tadikonda, M D Winniford, and S M Collins: Simultaneous detection of both coronary borders. IEEE Transactions on Medical Imaging, 12 (3), 1993.

    Google Scholar 

  82. T M Strat, and M A Fischler: Context-based vision: Recognizing objects using information from both 2D and 3D imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (10): 1050–1065, 1991.

    Article  Google Scholar 

  83. D Terzopoulos: Visual modeling. In Proceedings of the British Machine Vision Conference, Glasgow, Scotland, pages 9–11, Springer Verlag, London-Berlin-New York, 1991.

    Google Scholar 

  84. D Terzopoulos, and K Fleischer: Deformable models. The Visual Computer, 4 (6): 306–331, 1988.

    Article  Google Scholar 

  85. D Terzopoulos, and D Metaxas: Dynamic 3D models with local and global deformations: Deformable superquadrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (7): 703–714, 1991.

    Article  Google Scholar 

  86. D Terzopoulos, A Witkin, and M Kass: Symmetry-seeking models for 3D object reconstruction. In Proceedings, First International Conference on Computer Vision, London, England, pages 269–276, IEEE, Piscataway, NJ, 1987.

    Google Scholar 

  87. D Terzopoulos, A Witkin, and M Kass: Constraints on deformable models: Recovering 3D shape and nonrigid motion. Artificial Intelligence, 36 (1): 91–123, 1988.

    Article  MATH  Google Scholar 

  88. J C Tilton. Contextual classification on the massively parallel processor. In Frontiers of Massively Parallel Scientific Computation, Greenbelt, Md, pages 171–181, NASA, Washington, DC, 1987.

    Google Scholar 

  89. Toulson and Boyce 92] D L Toulson and J F Boyce. Segmentation of MR images using neural nets. Image and Vision Computing,10(5):324328, 1992.

    Google Scholar 

  90. J K Tsosos: Knowledge and the visual process. Pattern Recognition, 17 (1): 13–28, 1984.

    Article  Google Scholar 

  91. L Vincent, and P Soille: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEEPAMI, 13 (6): 583–598, 1991.

    Google Scholar 

  92. D L Waltz: Understanding line drawings of scenes with shadows. In The Psychology of Computer Vision. McGraw Hill, New York, 1957.

    Google Scholar 

  93. T Watanabe, and H Suzuki: An experimental evaluation of classifiers using spatial context for multispectral images. Systems and Computers in Japan, 19 (4): 33–47, 1988.

    Article  Google Scholar 

  94. T Watanabe, and H Suzuki: Compound decision theory and adaptive classification for multispectral image data. Systems and Computers in Japan, 20 (8): 37–47, 1989.

    Article  Google Scholar 

  95. H Wechsler. Computational Vision. Academic Press, London - San Diego, 1990.

    Google Scholar 

  96. S Wharton: A contextual classification method for recognising land use patterns in high resolution remotely sensed data. Pattern Recognition, 15: 317–324, 1982.

    Article  Google Scholar 

  97. G G Wilkinson, and J Megier: Evidential reasoning in a pixel classification hierarchy. A potential method for integrating image classifiers and expert system rules based on geographic context. International Journal of Remote Sensing, 11(10):1963–1968,1990.

    Google Scholar 

  98. D J Williams, and M Shah: A fast algorithm for active contours and curvature estimation. CVGIP - Image Understanding, 55 (1): 14–26, 1992.

    Article  MATH  Google Scholar 

  99. Winston 84] P H Winston: Artificial Intelligence. Addison-Wesley, Reading, Ma, 2nd edition, 1984.

    Google Scholar 

  100. A Witkin, D Terzopoulos, and M Kass: Signal matching through scale space. International Journal of Computer Vision, 1 (2): 133–144, 1987.

    Article  Google Scholar 

  101. C Zen, S Y Lin, and Y Y Chen. Parallel architecture for probabilistic relaxation operation on images. Pattern Recognition, 23 (6): 637–645, 1990.

    Article  Google Scholar 

  102. M C Zhang, R M Haralick, and J B Campbell: Multispectral image context classification using stochastic relaxation. IEEE Transactions on Systems, Man and Cybernetics, 20 (1): 128–140, 1990.

    Article  Google Scholar 

  103. S W Zucker: Vertical and horizontal processes in low level vision. In A R Hanson and E M Riseman, editors, Computer Vision Systems, pages 187–195, Academic Press, New York, 1978.

    Google Scholar 

  104. S W Zucker: Organization of curve detection: Coarse tangent fields and fine spline coverings. Neural Networks, 1 (1): 534, 1988.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. University of Iowa, Iowa City, Iowa, USA

    Milan Sonka PhD

  2. Czech Technical University, Prague, Czech Republic

    Vaclav Hlavac PhD

  3. University of Leeds, Leeds, England

    Roger Boyle DPhil, MBCS, CEng

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  1. Milan Sonka PhD
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  2. Vaclav Hlavac PhD
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  3. Roger Boyle DPhil, MBCS, CEng
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© 1993 Milan Sonka, Vaclav Hlavac and Roger Boyle

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Sonka, M., Hlavac, V., Boyle, R. (1993). Image understanding. In: Image Processing, Analysis and Machine Vision. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-3216-7_8

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