Design of circular dot pattern code (CDPC) for maximum information capacity and robustness on geometric distortion/noise

Abstract

One dimensional or linear bar code has been used for distribution purposes such as product information and distribution channel identification. Those linear bar codes can support only one directional code layout and also support limited code error detection capability. Two dimensional bar codes (e.g., QR code) extending one dimensional bar codes were developed in database and index based types. Database type barcodes embed full information bits and show weak recognition rate with geometric distortion. Index-based embed only the index information and requires additional network servers to interpret the index information, which leads to limited information storage capacity. Instead of using visible bar codes, we propose CDPC (circular dot pattern code), which is a dot based codes which is more invisible than bar codes. We design CDPC to be more robust to geometric distortion and noise than previous coding schemes using circular template matching. To maximize the information capacity and robustness, we use a circular dot patterns which is more robust to affine transformation. Code can be easily extended according to the number of data circles. If the number of data circle is n, then we can embed $$ \left( {5\sum {_{{k = 2}}^{{n + 1}}{\text{k}}} } \right) - {\text{n}} $$ data bits. In our experimentation, we set the number of circle to three, and resulting information capacity can be 42 bits per one code. To extract information from a CDPC codes, we perform (1) image capture, (2) identification of dots, (3) graph based topological analysis of dot patterns, (4) template matching between topological graphs using position symbols, and (5) information bit extraction with error correction capability. To evaluate information capacity under various geometric distortions, we experiment our CDPC with StirMark Benchmark's affine transformation (simulation of geometric and noise attacks) and with real cell phone image captures. Our experimental results also show that our CDPC scheme achieves more robust recognition performance than those proposed in previous research works including QR code.