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Machine Vision and Applications

, Volume 3, Issue 1, pp 1–11 | Cite as

Hierarchical image fusion

  • Alexander Toet
Article

Abstract

A hierarchical image fusion scheme is presented that preserves those details from the input images that are most relevant to visual perception. Results show that fused images present a more detailed representation of the scene and provide information that cannot be obtained by viewing the input images separately. Detection, recognition, and search tasks may therefore benefit from this fused image representation.

Key words

sensor fusion ratio of low-pass pyramid mathematical morphology contrast decomposition multiresolution image representations 

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References

  1. Burt PJ (1984) The pyramid as a structure for efficient computation. In: Rosenfeld A (ed) Multiresolution Image Processing and Analysis. Springer-Verlag, Berlin, pp 6–35Google Scholar
  2. Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Transactions on Communications COM-31(4):532–540Google Scholar
  3. Burt PJ, Adelson EH (1985) Merging images through pattern decomposition. In: Applications of Digital Image Processing VIII, Proceedings of SPIE 575, pp 173–181Google Scholar
  4. Burton GJ, Haig ND, Moorhead IR (1986) A self-similar stack model for human and machine vision. Biological Cybernetics 53:397–403Google Scholar
  5. Growley JL, Parker AC (1984) A representation for shape based on peaks and ridges in the difference of low-pass transform. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6:156–170Google Scholar
  6. Haralick RM, Lin C, Lee JSJ, Zhuang X (1987a) Multiresolution morphology. In: Proceedings of IEEE First International Conference on Computer Vision, IEEE Comp. Soc. Press, Washington, pp 516–520Google Scholar
  7. Haralick RM, Sternberg SR, Zhuang X (1987b) Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9(4):532–550Google Scholar
  8. Huang KS, Jenkins BK, Sawchuk AA (1989) Binary image algebra and optical cellular logic processor design. Computer Vision, Graphics and Image Processing 45:295–345Google Scholar
  9. Koenderink JJ (1984) The structure of images. Biological Cybernetics 50:363–370Google Scholar
  10. Koenderink JJ, Doorn AJ van (1978) Visual detection of spatial contrast; influence of location in the visual field, target extent and illuminance level. Biological Cybernetics 30:157–167Google Scholar
  11. Koenderink JJ, Doorn AJ van (1982) Invariant features of contrast detection: An explanation in terms of selfsimilar detector arrays. Journal of Optical Society of America, 72:83–87Google Scholar
  12. Maragos PA (1987) Tutorial on advances in morphological image processing and analysis. Optical engineering 26:623–632Google Scholar
  13. O Ying-Lie, Toet A (1990) Mathematical morphology in hierarchical image representation. In: Proceedings of NATO ASI. The Formation, Handling and Evaluation of Medical Images. Springer-Verlag, New York. In pressGoogle Scholar
  14. Rosenfeld A (ed) (1984) Multiresolution Image Processing and Analysis. Springer-Verlag, New YorkGoogle Scholar
  15. Serra J (1982) Image Analysis and Mathematical Morphology. Academic Press, New YorkGoogle Scholar
  16. Serra J (ed) (1988) Alternating sequential filters. In: Image Analysis and Mathematical Morphology, Vol. 2: Theoretical Advances, pp 203–216. Academic Press, New YorkGoogle Scholar
  17. Shih FY, Mitchell OR (1989) Threshold decomposition of gray-scale morphology into binary morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-11(1):31–42Google Scholar
  18. SPIDER Working Group (1983) SPIDER User's Manual. AIST MITI, JapanGoogle Scholar
  19. Toet A (1989a) Image fusion by a ratio of low-pass pyramid. Pattern Recognition Letters 9:245–253Google Scholar
  20. Toet A (1989b) A morphological pyramidal image decomposition. Pattern Recognition Letters 9:255–261Google Scholar
  21. Toet A (1990) Morphological Multiresolution Image Representations. Report TNO-IZF Institute for Perception TNOGoogle Scholar
  22. Toet A, Koenderink JJ, Zuidema P, Graaf CN de (1984) Image analysis-topological methods. In: DeConinck F (Ed) Information Processing in Medical Imaging. Martinus Nijhof, The Hague, pp 306–342Google Scholar

Copyright information

© Springer-Verlag New York Inc. 1990

Authors and Affiliations

  • Alexander Toet
    • 1
  1. 1.Institute for Perception TNOSoesterbergThe Netherlands

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