Abstract
The Image Foresting Transform (IFT) is a graph-based framework to develop image operators based on optimum connectivity between a root set and the remaining nodes, according to a given path-cost function. Oriented Image Foresting Transform (OIFT) was proposed as an extension of some IFT-based segmentation methods to directed graphs, enabling them to support the processing of global object properties, such as connectedness, shape constraints, boundary polarity, and hierarchical constraints, allowing their customization to a given target object. OIFT lies in the intersection of the Generalized Graph Cut and the General Fuzzy Connectedness frameworks, inheriting their properties. Its returned segmentation is optimal, with respect to an appropriate graph cut measure, among all segmentations satisfying the given constraints. In this work, we propose the Differential Oriented Image Foresting Transform (DOIFT), which allows multiple OIFT executions for different root sets, making the processing time proportional to the number of modified nodes. Experimental results show considerable efficiency gains over the sequential flow of OIFTs in image segmentation, while maintaining a good treatment of tie zones. We also demonstrate that the differential flow makes it feasible to incorporate area constraints in OIFT segmentation of multi-dimensional images.
Thanks to Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq – (Grant 407242/2021-0, 313087/2021-0, 465446/2014-0, 166631/2018-3), CAPES (88887.136422/2017-00) and FAPESP (2014/12236-1, 2014/50937-1).
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References
Bejar, H.H., Miranda, P.A.: Oriented relative fuzzy connectedness: theory, algorithms, and its applications in hybrid image segmentation methods. EURASIP J. Image Video Process. 2015(21) (2015)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vision 70(2), 109–131 (2006)
Ciesielski, K., Udupa, J., Falcão, A., Miranda, P.: A unifying graph-cut image segmentation framework: algorithms it encompasses and equivalences among them. In: Proceedings of SPIE on Medical Imaging: Image Processing, vol. 8314 (2012)
Ciesielski, K., Udupa, J., Saha, P., Zhuge, Y.: Iterative relative fuzzy connectedness for multiple objects with multiple seeds. Comput. Vision Image Underst. 107(3), 160–182 (2007)
Ciesielski, K.C., Falcão, A.X., Miranda, P.A.V.: Path-value functions for which Dijkstra’s algorithm returns optimal mapping. J. Math. Imaging Vision 60(7), 1025–1036 (2018)
Condori, M.A., Cappabianco, F.A., Falcão, A.X., Miranda, P.A.: An extension of the differential image foresting transform and its application to superpixel generation. J. Visual Commun. Image Represent. 71, 102748 (2020)
Cousty, J., Bertrand, G., Najman, L., Couprie, M.: Watershed cuts: minimum spanning forests and the drop of water principle. IEEE Trans. Pattern Anal. Mach. Intell. 31(8), 1362–1374 (2008)
Falcão, A.X., Bergo, F.P.: Interactive volume segmentation with differential image foresting transforms. IEEE Trans. Med. Imaging 23(9), 1100–1108 (2004)
Falcão, A., Stolfi, J., Lotufo, R.: The image foresting transform: theory, algorithms, and applications. IEEE TPAMI 26(1), 19–29 (2004)
Galvão, F.L., Falcão, A.X., Chowdhury, A.S.: RISF: recursive iterative spanning forest for superpixel segmentation. In: 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 408–415 (2018)
Leon, L.M., Ciesielski, K.C., Miranda, P.A.: Efficient hierarchical multi-object segmentation in layered graphs. Math. Morphol. Theory Appl. 5(1), 21–42 (2021). https://doi.org/10.1515/mathm-2020-0108
Mansilla, L.A.C., Miranda, P.A.V., Cappabianco, F.A.M.: Oriented image foresting transform segmentation with connectivity constraints. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2554–2558 (2016)
Mansilla, L., Miranda, P.: Image segmentation by oriented image foresting transform: Handling ties and colored images. In: 18th International Conference on Digital Signal Processing, Greece, pp. 1–6 (2013)
Miranda, P., Mansilla, L.: Oriented image foresting transform segmentation by seed competition. IEEE Trans. Image Process. 23(1), 389–398 (2014)
de Moraes Braz, C., Miranda, P.A., Ciesielski, K.C., Cappabianco, F.A.: Optimum cuts in graphs by general fuzzy connectedness with local band constraints. J. Math. Imaging Vision 62, 659–672 (2020)
Vargas-Muñoz, J.E., Chowdhury, A.S., Alexandre, E.B., Galvão, F.L., Miranda, P.A.V., Falcão, A.X.: An iterative spanning forest framework for superpixel segmentation. IEEE Trans. Image Process. 28(7), 3477–3489 (2019)
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Condori, M.A.T., Miranda, P.A.V. (2022). Differential Oriented Image Foresting Transform Segmentation by Seed Competition. In: Baudrier, É., Naegel, B., Krähenbühl, A., Tajine, M. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2022. Lecture Notes in Computer Science, vol 13493. Springer, Cham. https://doi.org/10.1007/978-3-031-19897-7_24
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