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A method to detect calibration patterns using fast adaptive binarization and plane homography for images having complicated backgrounds and taken under lighting condition of industrial fields is proposed. The preprocessing step of involving the calibration of a camera, as required to measure object dimensions, must be able to extract calibration points from a calibration pattern. However, proper lighting conditions for the camera calibration of a measurement system are rarely provided in industrial settings. In this paper, a plane homography of calibration points extracted by fast adaptive image binarization is used for the automatic detection of a local set of calibration points. The integral image method is used as a central part for the rapid processing of adaptive image binarization. The extracted point set provides a transformation matrix of the plane homography and guides the extraction of other calibration points for cluttered backgrounds with irregular lighting conditions. During the calibration of the camera, a calibration point filtering procedure by a data In/Out system iteratively selects more correct calibration points while rejecting outliers and noisy data. Experiments using actual images verify that the proposed method can be applied to camera calibration with poor quality images having cluttered backgrounds obtained under uneven illumination.

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-Among various decoding algorithms of low-density parity-check (LDPC) codes, the minsum (MS) algorithm and its modified algorithms are widely adopted because of their computational simplicity compared to the sum-product (SP) algorithm with slight loss of decoding performance. In the MS algorithm, the magnitude of the output message from a check node (CN) processing unit is decided by either the smallest or the next smallest input message which are denoted as min1 and min2, respectively. It has been shown that multiplying a scaling factor to the output of CN message will improve the decoding performance. Further, Zhong et al. have shown that multiplying different scaling factors (called a 2-dimensional scaling) to min1 and min2 much increases the performance of the LDPC decoder. In this paper, the simplified 2-dimensional scaled (S2DS) MS algorithm is proposed. In the proposed algorithm, we figure out a pair of the most efficient scaling factors which multiplications can be replaced with combinations of addition and shift operations. Furthermore, one scaling operation is approximated by the difference between min1 and min2. The simulation results show that S2DS achieves the error correcting performance which is close to or outperforms the SP algorithm regardless of coding rates, and its computational complexity is the lowest comparing to modified versions of MS algorithms.

-Meaningful logos or random sequences have been used in the current digital watermarking techniques of 2D bar code. The meaningful logos can not only be created by copyright holders based on their unique information, but are also very effective when representing their copyrights. The random sequences enhance the security of the watermark for verifying one's copyrights against intentional or unintentional attacks. In this paper, we propose a new watermarking technique taking advantage of Data Matrix as well as encryption keys. The Data Matrix not only recovers the original data by an error checking and correction algorithm, even when its high-density data storage and barcode are damaged, but also encrypts the copyright verification information by randomization of the barcode, including ownership keys. Furthermore, the encryption keys and the patterns are used to localize the watermark, and make the watermark robust against attacks, respectively. Through the comparison experiments of the copyright information extracted from the watermark, we can verify that the proposed method has good quality and is robust to various attacks, such as JPEG compression, filtering and resizing.

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