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- W2895929072 abstract "With the pervasive availability of LCD displays and phone cameras, screen-camera communication has attracted grate attentions due to the characteristics of convenience, security, infrastructure-free, and contactless. The existing screen-camera communication systems using dynamic barcodes suffer from poor ability of color recognition. In this paper, we propose a machine learning based multi-color dynamic barcode system called MMCode to overcome such limit. We formulate the color recognition problem as a machine learning task, and propose a semi-supervised clustering algorithm to achieve finer-grained color recognition. The proposed mechanism inserts reference colors in the barcode design, and adopts a semi-supervised Gaussian Mixed Model (GMM) algorithm for frame decoding. We implement MMCode as an Android APP and test its performance under real screen-camera communication scenarios. Extensive experiments show that the proposed MMCode achieves significant enhancement on the capacity of dynamic barcodes compared to the state-of-the-arts." @default.
- W2895929072 created "2018-10-26" @default.
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- W2895929072 date "2018-07-01" @default.
- W2895929072 modified "2023-09-23" @default.
- W2895929072 title "MMCode: Enhancing Color Channels for Screen-Camera Communication with Semi-Supervised Clustering" @default.
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- W2895929072 doi "https://doi.org/10.1109/icccn.2018.8487328" @default.
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