In this paper, we propose an improved method of embedding and detecting data in a printed image using a camera of a mobile device. The proposed method is based on the data diffusion method. We discuss several problems in the conventional lens distortion correction method. In addition, the possibility of using multiple captured images by employing a motion-image-capturing technique is also examined. A method of selecting captured images that are expected to obtain a high detection rate is also proposed. From the experimental results, it is shown that the proposed method is effective for improving data detection.

In information retrieval from printed images considering the use of mobile devices, the correction of geometrical deformation and lens distortion is required, posing a heavy computational burden. In this paper, we propose a method of reducing the computational burden for such corrections. This method consists of improved extraction to find a line segment of a frame, the reconsideration of the interpolation method for image correction, and the optimization of image resolution in the correction process. The proposed method can reduce the number of computations significantly. The experimental result shows the effectiveness of the proposed method.

In this paper, we propose an improved data embedding and extraction method for information retrieval considering the use of mobile devices. Although the conventional method has demonstrated good results for images captured by cellular phones, some problems remain with this method. One problem is the lack of consideration of the construction of the code grouping in the code grouping method. In this paper, a new construction method for code grouping is proposed, and it is shown that a suitable grouping of the codes can be found. Another problem is the correction method of lens distortion, which is time-consuming. Therefore, to improve the processing speed, the golden section search method is adopted to estimate the distortion coefficients. In addition, a new tuning algorithm for the gain coefficient in the embedding process is also proposed. Experimental results show an increase in the detection rate for embedding data and a reduction of the processing time.