Abstract
Mathematical morphology is a useful theory of nonlinear operators widely used for image processing and analysis. Despite the successful application of morphological operators for binary and gray-scale images, extending them to vector-valued images is not straightforward because there are no unambiguous orderings for vectors. Among the many approaches to multivalued mathematical morphology, those based on total orders are particularly promising. Morphological operators based on total orders do not produce the so-called false-colors. On the downside, they often introduce irregularities in the output image. Although the irregularity issue has a rigorous mathematical formulation, we are not aware of an efficient method to quantify it. In this paper, we propose to quantify the irregularity of a vector-valued morphological operator using the Wasserstein metric. The Wasserstein metric yields the minimal transport cost for transforming the input into the output image. We illustrate by examples how to quantify the irregularity of vector-valued morphological operators using the Wasserstein metric.
This work was supported in part by the São Paulo Research Foundation (FAPESP) under grant no 2019/02278-2 and the National Council for Scientific and Technological Development (CNPq) under grant no 310118/2017-4.
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References
Angulo, J.: Morphological Colour Operators in Totally Ordered Lattices Based on Distances: Application to Image Filtering, Enhancement and Analysis. Comput. Visi. Image Underst. 107(1–2), 56–73 (2007)
Aptoula, E., Lefèvre, S.: A Comparative Study on Multivariate Mathematical Morphology. Pattern Recogn. 40(11), 2914–2929 (2007)
Birkhoff, G.: Lattice Theory, 3rd edn. American Mathematical Society, Providence (1993)
Burgeth, B., Kleefeld, A.: An Approach to Color-Morphology Based on Einstein Addition and Loewner Order. Pattern Recogn. Lett. 47, 29–39 (2014)
Chevallier, E., Angulo, J.: The Irregularity Issue of Total Orders on Metric Spaces and Its Consequences for Mathematical Morphology. J. Math. Imaging Vis. 54(3), 344–357 (2015). https://doi.org/10.1007/s10851-015-0607-7
Dougherty, E.R., Lotufo, R.A.: Hands-on Morphological Image Processing. SPIE Press, Bellingham (2003)
van de Gronde, J., Roerdink, J.: Group-Invariant Colour Morphology Based on Frames. IEEE Trans. Image Process. 23(3), 1276–1288 (2014)
Heijmans, H.J.A.M.: Mathematical Morphology: A Modern Approach in Image Processing Based on Algebra and Geometry. SIAM Rev. 37(1), 1–36 (1995)
Lézoray, O.: Complete Lattice Learning for Multivariate Mathematical Morphology. J. Vis. Commun. Image Represent. 35, 220–235 (2016). https://doi.org/10.1016/j.jvcir.2015.12.017
Ronse, C.: Why Mathematical Morphology Needs Complete Lattices. Sig. Proc. 21(2), 129–154 (1990)
Rubner, Y., Tomasi, C., Guibas, L.J.: Earth Mover’s Distance as a Metric for Image Retrieval. Int. J. Comput. Vis. 40(2), 99–121 (11 2000). https://doi.org/10.1023/A:1026543900054. https://link.springer.com/article/10.1023/A:1026543900054
Sangalli, M., Valle, M.E.: Color mathematical morphology using a fuzzy color-based supervised ordering. In: Barreto, G.A., Coelho, R. (eds.) NAFIPS 2018. CCIS, vol. 831, pp. 278–289. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95312-0_24
Sangalli, M., Valle, M.E.: Approaches to multivalued mathematical morphology based on uncertain reduced orderings. In: Burgeth, B., Kleefeld, A., Naegel, B., Passat, N., Perret, B. (eds.) ISMM 2019. LNCS, vol. 11564, pp. 228–240. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20867-7_18
Serra, J.: The “false colour’’ problem. In: Wilkinson, M.H.F., Roerdink, J.B.T.M. (eds.) ISMM 2009. LNCS, vol. 5720, pp. 13–23. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03613-2_2
Soille, P.: Morphological Image Analysis. Springer, Berlin (1999). https://doi.org/10.1007/978-3-662-03939-7
Soille, P., Vogt, J., Colombo, R.: Carving and Adaptive Drainage Enforcement of Grid Digital Elevation Models. Water Resour. Res. 39(12), 1366 (2003)
Trussell, H.J., Vrhel, M.J.: Photometry and colorimetry. In: Fundamentals of Digital Imaging, pp. 191–244. Cambridge University Press, Cambridge (2008). https://doi.org/10.1017/CBO9780511754555.009
Valle, M.E., Valente, R.A.: Mathematical morphology on the spherical CIELab quantale with an application in color image boundary detection. J. Math. Imaging Vis. 57(2), 183–201 (2017). https://doi.org/10.1007/s10851-016-0674-4. https://link.springer.com/article/10.1007/s10851-016-0674-4
Velasco-Forero, S., Angulo, J.: Supervised ordering in Rp: application to morphological processing of hyperspectral images. IEEE Trans. Image Process. 20(11), 3301–3308 (2011). https://doi.org/10.1109/TIP.2011.2144611
Velasco-Forero, S., Angulo, J.: Random projection depth for multivariate mathematical morphology. IEEE J. Sel. Top. Sig. Process. 6(7), 753–763 (2012). https://doi.org/10.1109/JSTSP.2012.2211336
Velasco-Forero, S., Angulo, J.: Mathematical morphology for vector images using statistical depth. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 355–366. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21569-8_31
Velasco-Forero, S., Angulo, J.: Vector ordering and multispectral morphological image processing. In: Celebi, M.E., Smolka, B. (eds.) Advances in Low-Level Color Image Processing. LNCVB, vol. 11, pp. 223–239. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-007-7584-8_7
Villani, C.: Topics in Optimal Transportation. American Mathematical Society, Providence (2003)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
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Valle, M.E., Francisco, S., Granero, M.A., Velasco-Forero, S. (2021). Measuring the Irregularity of Vector-Valued Morphological Operators Using Wasserstein Metric. In: Lindblad, J., Malmberg, F., Sladoje, N. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2021. Lecture Notes in Computer Science(), vol 12708. Springer, Cham. https://doi.org/10.1007/978-3-030-76657-3_37
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