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A hardware-efficient parallel architecture for real-time blob analysis based on run-length code

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Abstract

Blob analysis has been extensively used in target detection, object recognition, moving target tracking, among other applications. Because blob analysis is computationally expensive, it has become a bottleneck of real-time applications. To tackle this problem, a parallel algorithm for blob analysis is proposed, and a hardware-efficient architecture for this algorithm is presented in this paper. First, based on image data partition and multi-process units, a novel parallel algorithm of blob analysis is proposed to process objects with different types and sizes. Second, a dynamic convex hull calculation method is designed, which is highly efficient for parallel processing and sub-block merging of connected component labeling. Third, a parallel hardware structure of the proposed algorithm is designed and implemented on FPGA. To evaluate performance, blobs of different types and sizes are located by the proposed algorithm in software and hardware. The experimental results demonstrate that the blobs are effectively and correctly located by the proposed algorithm, and the proposed hardware architecture works more efficiently than the state-of-the-art methods.

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Acknowledgments

This work was partially supported by the National Key Research and Development Program of China (Grant No. 2016YFC0801003), and National Natural Science Foundation of China (No. 61272347).

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Authors and Affiliations

  1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Bingjie Li, Cunguang Zhang, Bo Li, Hongxu Jiang & Qizhi Xu

  2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Bo Li, Hongxu Jiang & Qizhi Xu

Authors
  1. Bingjie Li
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  2. Cunguang Zhang
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  3. Bo Li
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  4. Hongxu Jiang
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  5. Qizhi Xu
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Correspondence to Hongxu Jiang.

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Li, B., Zhang, C., Li, B. et al. A hardware-efficient parallel architecture for real-time blob analysis based on run-length code. J Real-Time Image Proc 15, 657–672 (2018). https://doi.org/10.1007/s11554-017-0709-0

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  • DOI: https://doi.org/10.1007/s11554-017-0709-0

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