Abstract
The Hit-or-Miss transform (HMT) is a well-known morphological operator for template matching in binary images. A novel approach for HMT for multivariate images is introduced in this paper. The generic framework is a generalization of binary case based on a h-supervised ordering formulation which leads to reduced orderings. In particular, in this paper we focus on the application of HMT for target detection on high-resolution images. The visual results of the experiments show the performance of proposed approach.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sim, D., Kwon, O., Park, R.: Object matching algorithms using robust hausdorff distance measures. IEEE Trans. Image Processing 8, 425–429 (1999)
Zhu, Z., Tang, M., Lu, H.: A new robust circular gabor based object matching by using weighted hausdorff distance. Pattern Recognition Letters 25, 515–523 (2004)
Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, Inc., Orlando (1983)
Soille, P.: Morphological Image Analysis. Springer, Heidelberg (1999)
Naegel, B., Passat, N., Ronse, C.: Grey-level hit-or-miss transforms - Part I: Unified theory. Pattern Recognition 40, 635–647 (2007)
Ronse, C.: A lattice-theoretical morphological view on template extraction in images. Journal of Visual Comm. and Image Representation 7, 273–295 (1996)
Aptoula, E., Lefevre, S., Ronse, C.: A hit-or-miss transform for multivariate images. Pattern Recognition Letters 30, 760–764 (2009)
Weber, J., Lefevre, S.: A multivariate Hit-or-Miss transform for conjoint spatial and spectral template matching. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 226–235. Springer, Heidelberg (2008)
Velasco-Forero, S., Angulo, J.: Morphological processing of hyperspectral images using kriging-based supervised ordering. In: ICIP (2010)
Roman, S.: Lattices and Ordered Sets. Springer, Heidelberg (2008)
Heijmans, H.: Theoretical aspects of gray-level morphology. IEEE Trans. Pattern Analysis and Machine Intelligence 13, 568–582 (1991)
Goutsias, J., Heijmans, H., Sivakumar, K.: Morphological operators for image sequences. Comput. Vis. Image Underst. 62, 326–346 (1995)
Barnett, V.: The ordering of multivariate data (with discussion). Journal of the Royal Statistical Society Series A 139, 318–354 (1976)
Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)
Lezoray, O., Charrier, C., Elmoataz, A.: Learning complete lattices for manifold mathematical morphology. In: Proc. of the ISMM, pp. 1–4 (2009)
Angulo, J.: Morphological colour operators in totally ordered lattices based on distances: Application to image filtering, enhancement and analysis. Comput. Vis. Image Underst. 107, 56–73 (2007)
Cristianini, N., Shawe-Taylor, J.: An Introduction to support vector machines and other kernel based learning methods. Cambridge University Press, Cambridge (2000)
Regazzoni, C., Teschioni, A.: A new approach to vector median filtering based on space filling curves. IEEE Trans. Image Processing 6, 1025–1037 (1997)
Chanussot, J., Lambert, P.: Extending mathematical morphology to color image processing. In: Int. Conf. on Color in Graphics and Image Proc., pp. 158–163 (2000)
Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, Inc., Orlando (1982)
Goutsias, J., Heijmans, H.: Mathematical Morphology. IOS Press, Amsterdam (2000)
Haralick, R., Sternberg, S., Zhuang, X.: Image analysis using mathematical morphology. IEEE Trans. Pattern Analysis and Machine Intelligence 9, 532–550 (1987)
Aptoula, E., Lefèvre, S.: A comparative study on multivariate mathematical morphology. Pattern Recognition 40, 2914–2929 (2007)
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
Velasco-Forero, S., Angulo, J. (2010). Hit-or-Miss Transform in Multivariate Images. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17688-3_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-17688-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17687-6
Online ISBN: 978-3-642-17688-3
eBook Packages: Computer ScienceComputer Science (R0)