The Panoramic Visualization of Metallic Materials in Macro- and Microstructure of Surface Analysis Using Microsoft Image Composite Editor (ICE)

  • Anna Wójcicka
  • Zygmunt Wróbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7339)


Nowadays, digital photography is ubiquitous and indispensable tool used in various fields of knowledge. In addition to standard photography and more and more interesting kind of photos are used panoramic pictures. Completely new use of such images is to use them for panoramic visualization of the structure of materials - high resolution allows an insight into macro and microstructure of the material surface. The use of panoramic visualization of the surface of metallic materials for macroscopic and microscopic analysis of the structure of materials is a tool of immense possibilities that successfully is widely used in structural studies of materials in various fields of science. The article presents the disadvantages and advantages of the creation and use of panoramic images, acquiring images and demonstrates how the implementation of the panorama using Microsoft Image Composite Editor (ICE) on the example of the sample surface joints of metallic materials.


Panoramic visualization of metallic materials Panoramic photo Microsoft Image Composite Editor MS ICE 


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  1. 1.
    Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., Cohen, M.: Interactive Digital Photomontage. ACM Transactions on Graphics 23(3) (2004)Google Scholar
  2. 2.
    Benicewicz-Miazga, A., Klauzinski, E., Góra, A.: Cyfrowa fotografia panoramiczna. Helion, Gliwice (2001)Google Scholar
  3. 3.
    Freeman, M.: Fotografia cyfrowa. 101 praktycznych wskazówek. National Geographic (2009)Google Scholar
  4. 4.
    Górecki, M.: 10 projektów w cyfrowej ciemni fotograficznej. Helion, Gliwice (2007)Google Scholar
  5. 5.
    Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. Computer Vision and Pattern Recognition (2004)Google Scholar
  6. 6.
    Kelby, S.: Fotografia cyfrowa. Edycja zdjęć, 6th edn., Helion, Gliwice (2011)Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence 27(10) (2005)Google Scholar
  9. 9.
    Szeliski, R., Uyttendaele, M., Steedly, D.: Fast Poisson blending using multi-splines. In: International Conference on Computational Photography (2011)Google Scholar
  10. 10.
    Weinmann, E., Lourekas, P.: Photoshop CS5 for Windows and Macintosh: Visual QuickStart Guide. Peachpit Press (2010) 978-0321701534 Google Scholar
  11. 11.
    Winder, S., Hua, G., Brown, M.: Picking the best DAISY. In: Proceedings of CVPR, pp. 178–185 (2009)Google Scholar
  12. 12.
    Wróbel, Z., Koprowski, R.: Praktyka przetwarzania obrazów z zadaniami w programie Matlab, p. 278. Akademicka Oficyna Wydawnicza ”Exit”, Warszawa (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anna Wójcicka
    • 1
  • Zygmunt Wróbel
    • 2
  1. 1.Department of Technology and Engineering of Material, Institute of TechnologyPedagogical UniversityCracowPoland
  2. 2.Department of Computer Biomedical Systems, Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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