Vision-based 1D barcode reading has been the subject of extensive research in recent years due to the high demand for automation in various industrial settings. With the aim of detecting the image region of 1D barcodes, existing approaches are both slow and imprecise. Deep-learning-based methods can locate the 1D barcode region fast but lack an adequate and accurate segmentation process; while simple geometric-based techniques perform weakly in terms of localization and take unnecessary computational cost when processing high-resolution images. We propose integrating the deep-learning and geometric approaches with the objective of tackling robust barcode localization in the presence of complicated backgrounds and accurately detecting the barcode within the localized region. Our integrated real-time solution combines the advantages of the two methods. Furthermore, there is no need to manually tune parameters in our approach. Through extensive experimentation on standard benchmarks, we show that our integrated approach outperforms the state-of-the-art methods by at least 5%.

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Finding optimal configurations in a geometric space is a key challenge in many technological disciplines. Current approaches either rely heavily on human domain expertise and are difficult to scale. In this paper we show it is possible to solve configuration optimization problems for whole-page recommendation using reinforcement learning. The proposed Tile Networks is a neural architecture that optimizes 2D geometric configurations by arranging items on proper positions. Empirical results on real dataset demonstrate its superior performance compared to traditional learning to rank approaches and recent deep models.

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