Abstract
The use of fusion systems has known a wide growth and they now need reliable ways to evaluate their performance. Fusion systems are complex because they involve a complete information treatment chain (from the information extraction to the decision). This paper studies the different approaches used for system evaluation and proposes a local evaluation method for the evaluation of each subpart of the fusion system. The approach is then illustrated on cooperative fusion system devoted to 3D image interpretation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Levin, M.S.: System synthesis with morphological clique problem: fusion of subsystem evaluation decisions. Information Fusion 2, 225–237 (2001)
Fawcett, T.: Roc graphs with instance-varying costs. Pattern Recognition Letters 27, 882–891 (2006)
Cvejic, N., Loza, A., Bull, D.: A similarity metric for assessment of image fusion algorithms. Journal of Signal Processing 2(3), 178–182 (2005)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity, vol. 13(4), pp. 600–612 (2004)
Wang, Z., Bovik, A.C.: A universal image quality index. International Journal of Signal Processing 9(3), 81–84 (2002)
Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proc. International Conference on Image Processing ICIP 2003, September 14-17, vol. 3, pp. III–173–176 (2003)
Jullien, S., Valet, L., Mauris, G., Bolon, P., Teyssier, S.: An attribute fusion system based on the choquet integral to evaluate the quality of composite parts. IEEE Trans. On Instrumentation and Measurement 57(4), 755–762 (2008)
Grabisch, M.: The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research 89, 445–456 (1996)
Toet, A., Franken, E.M.: Perceptual evaluation of different image fusion schemes. Displays 24(1), 25–37 (2003)
Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics Letters 38(7), 313–315 (2002)
Xydeas, C.S., Petrovic, V.: Objective image fusion performance measure. Electronics Letters 36(4), 308–309 (2000)
Cardoso, J.S., Corte-Real, L.: Toward a generic evaluation of image segmentation, vol. 14(11), pp. 1773–1782 (2005)
Cha, S.-H., Srihari, S.N.: On measuring distance between histograms. Pattern Recognition 35, 1355–1370 (2002)
Lamallem, A., Valet, L., Coquin, D.: Local versus global evaluation of a cooperative fusion system for 3D image interpretation. In: IEEE International Symposium on Optomechatronic Technologies, Istanbul, Turkey, pp. 360–365 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lamallem, A., Valet, L., Coquin, D. (2010). Performance Evaluation of a Fusion System Devoted to Image Interpretation. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-14049-5_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14048-8
Online ISBN: 978-3-642-14049-5
eBook Packages: Computer ScienceComputer Science (R0)