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Global suppression heuristic: fast GraphCut in GPU for image stitching

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Abstract

GraphCut algorithm has shown its effectiveness when solving many computer vision tasks. However, its heavy computational nature makes it hard to apply in real-world applications. Many attempts have been made to accelerate GraphCut algorithm, most successfully seen in methods that utilize parallel computing platforms like CUDA. In this paper, we introduce a parallel implementation of push relabel algorithm for GraphCut on CUDA designed for the image stitching problem. Furthermore, we propose global suppression heuristic to boost the convergence process of the algorithm. Experiment results on sets of thermal infrared and RGB images show that our method can be up to 3 times faster than the fastest sequential algorithm while obtaining satisfactory stitched images. Our source code will be soon available for further research.

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All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Authors and Affiliations

  1. Viettel Aerospace Institute, Viettel Group, Hanoi, Vietnam

    Minh Bui, Tai Nguyen, Huong Ninh, Tu Nguyen & Tien Hai Tran

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  1. Minh Bui
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  2. Tai Nguyen
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  3. Huong Ninh
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  4. Tu Nguyen
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  5. Tien Hai Tran
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Contributions

TN and MB conducted analyses and proposed the global suppression heuristic and early stopping strategy. TN, TN, THT, and MB participated in constructing the code base for experiments. TN provided experiment results in terms of figures and tables. MB wrote the main manuscript text. HN participated in the design of the study. All authors reviewed the manuscript.

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Correspondence to Minh Bui.

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Bui, M., Nguyen, T., Ninh, H. et al. Global suppression heuristic: fast GraphCut in GPU for image stitching. SIViP 17, 2671–2678 (2023). https://doi.org/10.1007/s11760-023-02483-5

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  • DOI: https://doi.org/10.1007/s11760-023-02483-5

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