A linked list run-length-based single-pass connected component analysis for real-time embedded hardware

Conventional connected component analysis (CCA) algorithms render a slow performance in real-time embedded applications due to multiple passes to resolve label equivalences. As this fundamental task becomes crucial for stream processing, single-pass algorithms were introduced to enable a stream-oriented hardware design. However, most single-pass CCA algorithms in the literature inhibit maximum streaming throughput as additional time such as horizontal blanking period is required to resolve label equivalence. This paper proposes a novel single-pass CCA algorithm, using a combination of linked list and run-length-based techniques to label and resolve equivalences as well as extracting the object features in a single raster scan. The proposed algorithm involves a label recycling scheme which attains low memory requirement design. Experimental results show the implementation of the proposed CCA achieves one cycle per pixel throughput and surpasses the most memory-efficient state-of-the-art work up to 25 % reduction in memory usage for $$7680\times 4320$$7680×4320 pixels image.

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