Abstract
Morphological granulometries constitute one of the most useful and versatile image analysis techniques applied to a wide range of tasks, from size distribution of objects, to feature extraction and to texture characterization in industrial and research applications where high-performance instrumentation and online signal processing are required. Since granulometries are based on sequences of openings with structuring elements (SEs) of increasing size, they are computational demanding on non-specialized hardware. In this paper, a pipelined hardware architecture for fast computation of gray-level morphological granulometries is presented, centered around two systolic-like processing arrays able to process with flat SEs of different shapes and sizes. To validate the proposed scheme, the architecture was modeled, simulated and implemented into a field programmable gate array. Implementation results show that the architecture is able to compute particle size distribution on 512 × 512 sized images with flat non-rectangular SEs of up to 51 × 51, in around 60 ms at a clock frequency of 260 MHz. It is shown that a speed up over two orders of magnitude is obtained compared to a naive software implementation. The architecture performance compares favorably to similar hardware architectural schemes and to optimized high-performance graphical processing units-based implementations.
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Bartovský, J., Dokládal, P., Dokládalová, E., Georgiev, V.: Parallel implementation of sequential morphological filters. J. Real-Time Image Process. (2011). doi:10.1007/s11554-011-0226-5
Chien, S.Y., Ma, S.Y., Chen, L.G.: Partial-result-reuse architecture and its design technique for morphological operations with flat structuring elements. IEEE Trans. Circuits Syst. Video Technol. 15(9), 1156–1169 (2005). doi:10.1109/TCSVT.2005.852622
Déforges, O., Normand, N., Babel, M.: Fast recursive grayscale morphology operators: from the algorithm to the pipeline architecture. J. Real-Time Image Process. 8(2), 143–152 (2013). doi:10.1007/s11554-010-0171-8
Devaux, M.F., Bouchet, B., Legland, D., Guillon, F., Lahaye, M.: Macro-vision and grey level granulometry for quantification of tomato pericarp structure. Postharvest Biol. Technol. 47(2), 199–209 (2008). doi:10.1016/j.postharvbio.2007.06.017
Dougherty, E., Lotufo, R.: Hands-on Morphological Image Processing. SPIE Press, Bellingham (2003)
Gil, J., Kimmel, R.: Efficient dilation, erosion, opening, and closing algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1606–1617 (2002). doi:10.1109/TPAMI.2002.1114852
Gil, J., Werman, M.: Computing 2-d min, median, and max filters. IEEE Trans. Pattern Anal. Mach. Intell. 15(5), 504–507 (1993). doi:10.1109/34.211471
Hedberg, H., Kristensen, F., Owall, V.: Low-complexity binary morphology architectures with flat rectangular structuring elements. IEEE Trans. Circuits Syst. I Regul. Pap. 55(8), 2216–2225 (2008). doi:10.1109/TCSI.2008.918140
van Herk, M.: A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels. Pattern Recogn. Lett. 13(7), 517–521 (1992). doi:10.1016/0167-8655(92)90069-C
Karas, P., Morard, V., Bartovský, J., Grandpierre, T., Dokládalová, E., Matula, P., Dokládal, P.: Gpu implementation of linear morphological openings with arbitrary angle. J. Real-Time Image Process. 1–15. doi:10.1007/s11554-012-0248-7
Kim, H., Maruta, R., Huanca, D., Salcedo, W.: Correlation-based multi-shape granulometry with application in porous silicon nanomaterial characterization. J. Porous Mater. 1–11. http://dx.doi.org/10.1007/s10934-012-9607-9
Ko, S.J., Morales, A., Lee, K.H.: A fast implementation algorithm and a bit-serial realization method for grayscale morphological opening and closing. IEEE Trans. Signal Process. 43(12), 3058–3061 (1995). doi:10.1109/78.476966
Ljungqvist, M.G., Nielsen, M.E., Ersboll, B.K., Frosch, S.: Image analysis of pellet size for a control system in industrial feed production. PLoS One 6(10), e26,492 (2011). doi:10.1371/journal.pone.0026492
Maragos, P.: Pattern spectrum and multiscale shape representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 701–716 (1989). doi:10.1109/34.192465
Matheron, G.: Random Sets and Integral Geometry, vol. 1. Wiley, New York (1975)
Mavilio, A., Fernández, M., Trivi, M., Rabal, H., Arizaga, R.: Characterization of a paint drying process through granulometric analysis of speckle dynamic patterns. Signal Process. 90(5), 1623–1630 (2010). doi:10.1016/j.sigpro.2009.11.010
Morard, V., Dokladal, P., Decenciere, E.: One-dimensional openings, granulometries and component trees in o(1) per pixel. IEEE J. Sel. Topics Signal Process. 6(7), 840–848 (2012)
Serra, J.: Image Analysis and Mathematical Morphology, vol. 1. Academic Press, Inc., Orlando (1983)
Shih, F.Y.: Image processing and mathematical morphology fundamentals and applications. CRC Press, Boca Raton (2009)
Thurley, M., Danell, V.: Fast morphological image processing open-source extensions for GPU processing with CUDA. IEEE J. Sel. Topics Signal Process. 6(7), 849–855 (2012). doi:10.1109/JSTSP.2012.2204857
Torres-Huitzil, C., Arias-Estrada, M.: FPGA-based configurable systolic architecture for window-based image processing. EURASIP J. Adv. Signal Process. 2005(7), 1024–1034 (2005)
Urbach, E., Wilkinson, M.: Efficient 2-d grayscale morphological transformations with arbitrary flat structuring elements. IEEE Trans. Image Process. 17(1), 1–8 (2008). doi:10.1109/TIP.2007.912582
Urbach, E.R., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 272–285 (2007). doi:10.1109/TPAMI.2007.28
Van Droogenbroeck, M., Buckley, M.: Morphological erosions and openings: fast algorithms based on anchors. J. Math. Imaging Vision 22 (2–3), 121–142 (2005). http://hdl.handle.net/2268/1302 (special issue on mathematical morphology after 40 years)
Vincent, L.: Granulometries and opening trees. Fundam. Inf. 41(1–2), 57–90 (2000). http://dl.acm.org/citation.cfm?id=341148.341157
Zhang, C., Wang, C., Ahmad, M.: A pipeline vlsi architecture for fast computation of the 2-d discrete wavelet transform. IEEE Trans. Circuits Syst. I Regul. Pap. 59(8), 1775–1785 (2012). doi:10.1109/TCSI.2011.2180432
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The author acknowledges the support received from CONACyT, Mexico, through project No. 99912, and the anonymous reviewers for their comments to improve the quality of the paper.
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Torres-Huitzil, C. FPGA-based fast computation of gray-level morphological granulometries. J Real-Time Image Proc 11, 547–557 (2016). https://doi.org/10.1007/s11554-013-0355-0
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DOI: https://doi.org/10.1007/s11554-013-0355-0