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- Hung-Kuo Chu
- National Tsing Hua University
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- Chia-Sheng Chang
- National Tsing Hua University
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- Ruen-Rone Lee
- National Tsing Hua University
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- Niloy J. Mitra
- University College London
抄録
<jats:p> QR code is a popular form of barcode pattern that is ubiquitously used to tag information to products or for linking advertisements. While, on one hand, it is essential to keep the patterns machine-readable; on the other hand, even small changes to the patterns can easily render them unreadable. Hence, in absence of any computational support, such QR codes appear as random collections of black/white modules, and are often visually unpleasant. We propose an approach to produce high quality visual QR codes, which we call <jats:italic>halftone QR codes</jats:italic> , that are still machine-readable. First, we build a pattern readability function wherein we learn a probability distribution of what modules can be replaced by which other modules. Then, given a text tag, we express the input image in terms of the learned dictionary to encode the source text. We demonstrate that our approach produces high quality results on a range of inputs and under different distortion effects. </jats:p>
収録刊行物
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- ACM Transactions on Graphics
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ACM Transactions on Graphics 32 (6), 1-8, 2013-11
Association for Computing Machinery (ACM)