Image Fusion Based on Topographic Mappings Using the Hyperbolic Space

Information Visualization

Volumn 4, Issue 4, 1997, Pages 266-275

  Saalbach, Axel ^a   Ontrup, Jörg ^a   Ritter, Helge ^a   Nattkemper, Tim W ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

hyperbolic self organizing map; Image fusion; pseudocoloring; topographic mapping

Indexed keywords

EID: 84996166753     PISSN: 14738716     EISSN: 14738724     Source Type: Journal    
DOI: 10.1057/palgrave.ivs.9500106     Document Type: Article

References (42)

1. Richards JA. Remote Sensing Digital Image Analysis: An Introduction, 2nd edn. Springer: Berlin, Heidelberg, New York, 1993.
2. Bankman IN, (Ed). Handbook of Medical Imaging: Processing and Analysis. Academic Press: San Diego, San Francisco, New York, Boston, London, Sydney, Tokyo, 2000.
3. Wismüller A, Lange O, Dersch DR, Leinsinger GL, Hahn K, Pütz B, Auer D. Cluster analysis of biomedical image time-series. International Journal of Computer Vision 2002; 46: 103–128.
4. Landgrebe D. Information extraction principles and methods. In: ChenCH,(Ed). Information Processing for Remote Sensing. World Scientific Publishing Co: River Edge, New Jersey, 2000; 3–38.
5. Schumann H, Müller W. Visualisierung - Grundlagen und allgemeine Methoden. Springer: Berlin, Heiderberg, New York, 2000.
6. Villmann T, Merényi E, Hammer B. Neural maps in remote sensing image analysis. Neural Networks 2003; 16: 389–403.
7. Pohl C, van Genderen JL. Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing 1998; 19: 823–854.
8. Simone G, Farina A, Morabito FC, Serpico SB, Bruzzone L. Image fusion techniques for remote sensing applications. Information Fusion 2002; 3: 3–15.
9. Harikumar G, Bresler Y. Feature extraction techniques for exploratory visualization of vector-valued imagery. IEEE Transactions on Image Processing 1996; 5: 1324–1334.
10. Kohonen T. Self-Organizing Maps. Springer Series in Information Science, 3rd edn. Springer: Berlin, Heidelberg, New York, 2001.
11. Geladi P, Grahn H. Multivariate Image Analysis. Wiley: Chichester, 1996.
12. Fung T, LeDrew E. Application of principal component analysis to change detection. Photogrammetric Engineering & Remote Sensing 1987; 53: 1649–1659.
13. Schmiedl U, Orthendahl DA, Mark AS, Berry I, Kaufman L. The utility of principal component analysis for the image display of brain lesions: A preliminary, comparative study. Magnetic Resonance in Medicine 1987; 4: 471–486.
14. Groß MH, Seibert F. Visualization of multidimensional image data sets using a neural network. The Visual Computer 1993; 1: 145–159.
15. Koua E, Kraak MJ. Geovisualization to support the exploration of large health and demographic survey data. International Journal of Health Geographics 2004; 3: 12.
16. Ritter H. Self-organizing Maps in non-euclidean Spaces. In: OjaE, KaskiS,(Eds). Kohonen Maps. Elsevier: Amsterdam, 1999; 97–108.
17. Ontrup J, Ritter H. Text categorization and semantic browsing with self-organizing maps on non-euclidean spaces. In: Proceedings of PKDD-2001. Springer: Berlin, 2001; 338–349.
18. Morgan F. Riemannian Geometry: A Beginner’s Guide. Jones and Bartlett Publishers: Boston, London, 1993.
19. Lamping J, Rao R, Pirolli P. A focus+content technique based on hyperbolic geometry for viewing large hierarchies. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, Denver. ACM: New York, 1995; 401–408.
20. Munzner T. Exploring large graphs in 3D hyperbolic space. IEEE Computer Graphics and Applications 1998; 18: 18–23.
21. Fricke R, Klein F. Vorlesungen über die Theorie der automorphen Funktionen, Vol. 1. Teubner: Leipzig 1897 (Reprinted by Johnson Reprint: New York, 1965).
22. Magnus W. Noneuclidean Tesselations and Their Groups. Academic Press: New York, 1974.
23. Cannon JW, Floyd WJ, Kenyon R, Parry WR. Hyperbolic geometry. In: LevyS,(Ed). Flavors of Geometry. Cambridge University Press: Cambridge and New York, 1997.
24. Coxeter HSM. Non Euclidean Geometry. University of Toronto Press: Toronto, 1957.
25. Walter J. H-MDS: A new approach for interactive visualization with multidimensional scaling in the hyperbolic space. Information Systems 2004; 29: 273–292.
26. Rheingans P. Task-based color scale design. In: Proceedings of Applied Image and Pattern Recognition. SPIE Press: Bellingham, Washington, 1999; 35–43.
27. Li H, Burgess EA. Evaluation of signal detection performance with pseudocolour display and lumpy backgrounds. In: Proceedings of Medical Imaging. SPIE Press: Bellingham, Washington, 1997; 143–149.
28. Trumbo B. Theory for coloring bivariate statistical maps. The American Statistician 1981; 35: 220–226.
29. Harrower MA, Brewer CA. ColorBrewer.org: An online tool for selecting color schemes for maps. The Cartographic Journal 2003; 40: 27–37.
30. Levkowitz H, Herman GT. Color scales for image data. IEEE Computer Graphics & Applications 1992; 12: 72–80.
31. Rogowitz BE, Treinish LA. An Architecture for Perceptual Rule-Based Visualization. In: Proceedings of the IEEE Visualization 1993 Conference. IEE Publications, 1993; 236–243.
32. Ware C. Information Visualization, 2nd edn. Morgan Kaufmann: San Francisco, California, 2004.
33. Rheingans P. Dynamic color mapping of bivariate qualitative data. In: Proceedings of IEEE Visualization 1997. IEEE Computer Society Press: Los Alamitos, California, 1997; 159–166.
34. Smith AR. Color gamut transform pairs. In: Proceedings of SIGGRAPH 1978, ACM Press: New York, 1978; 12–19.
35. Hellwig G, Brix G, Griebel J, Lucht R, Delorme S, Siebert M, Englmeier KH. Dynamic MR mammography: Three-dimensional real-time visualization of contrast enhancement in virtual reality. Academic Radiology 2002; 9: 1255–1263.
36. Kuhl CK, Mielcareck P, Klaschik S, Leutner C, Wardelmann E, Gieseke J, Schild HH. Dynamic breast MR imaging: Are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 1999; 1: 101–110.
37. Wong P, Bergeron R. 30 Years of multidimensional multivariate visualization. In: NielsonGM, HagenH, MuellerH,(Eds). Scientific Visualization - Overviews, Methodologies and Techniques. IEEE Computer Society Press: Los Alamitos, California, 1997.
38. Fawcett T. ROC Graphs: notes and practical considerations for researchers. Technical report, HP Labs Tech Report HPL-2003–4, 2003.
39. Bauer HU, Pawelzik K. Quantifying the neighbourhood preservation of self-organizing feature maps. IEEE Transactions on Neural Networks 1992; 8: 226–233.
40. Goodhill GJ, Sejnowsky TJ. A unifying objective function for topographic mappings. Neural Computation 1997; 9: 1291–1303.
41. Bezdek JC, Pal NR. An Index of topological preservation for feature extraction. Pattern Recognition 1995; 28: 381–391.
42. Bauer HU, Villmann T. Growing a hypercubical output space in self-organizing feature maps. IEEE Transactions on Neural Networks 1997; 8: 218–226.