Abstract
The subject of this chapter is image fusion techniques which rely on simple pixel-by-pixel operations. The techniques include the basic arithmetic operations, logic operations and probabilistic operations as well as slightly more complicated mathematical operations. The image values include pixel gray-levels, feature map values and decision map labels. Although more sophisticated techniques are available, the simple pixel operations are still widely used in many image fusion applications.
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
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Patt. Anal. Mach. Intell. 28, 2037–2041 (2006)
Bazi, Y., Bruzzone, L., Melgani, F.: Image thresholding based on the em algorithm and the generalized Gaussian distribution. Patt. Recogn. 40, 619–634 (2007)
Bertalmio, M., Caselles, V., Pardo, A.: Movie denoising by average of warped lines. IEEE Trans. Image Process. 16, 2333–2347 (2007)
Bruzzone, L., Prieto, D.F.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 38, 1171–1182 (2000)
Chung, K.-L., Lin, Y.-R., Huang, Y.-H.: Efficient shadow detection of color aerial images based on successive thresholding scheme. IEEE Trans. Geosci. Remote Sens. 47, 671–682 (2009)
Hesse, C.W., Holtackers, D., Heskes, T.: On the use of mixtures of Gaussians and mixtures of generalized exponentials for modelling and classification of biomedical signals. In: IEEE Benelux EMBS Symposium (2006)
Monwar, M.M., Gavrilova, M.L.: Multimodal biometric system using rank-level fusion approaches. IEEE Trans. Syst. Man Cybernetics 39B, 867–878 (2009)
Rohlfing, T., Maurer Jr., C.R.: Shape-based averaging. IEEE Trans. Image Process. 16, 153–161 (2007)
Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)
Tsai, V.J.D.: A comparative study on shadow compensation of color aerial images in invariant color models. IEEE Trans. Geosci. Remote Sens. 44, 16671–16671 (2006)
Tu, T.-M., Su, S.-C., Shyu, H.-C., Huang, P.S.: A new look at IHS-like image fusion methods. Inf. Fusion 2, 177–186 (2001)
Wang, Z., Gao, C., Tian, J., Lia, J., Chen, X.: Multi-feature distance map based feature detection of small infra-red targets with small contrast in image sequences. In: Proc. SPIE, vol. 5985 (2005)
Wang, X., Yang, C., Zhou, J.: Spectral aggregation for clustering ensemble. In: Proc. Int. Conf. Patt. Recog. (2008)
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Mitchell, H.B. (2010). Pixel Fusion. In: Image Fusion. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11216-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-11216-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11215-7
Online ISBN: 978-3-642-11216-4
eBook Packages: EngineeringEngineering (R0)