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FPGA-based fast computation of gray-level morphological granulometries

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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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Acknowledgments

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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  1. CINVESTAV-Tamaulipas, Parque Científico y Tecnológico TECNOTAM, Km. 5.5 carretera Cd. Victoria-Soto La Marina, C.P. 87130, Cd. Victoria, Tamaulipas, Mexico

    Cesar Torres-Huitzil

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  1. Cesar Torres-Huitzil
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Correspondence to Cesar Torres-Huitzil.

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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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