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A local color image watermarking scheme using adaptive segmentation-based feature extraction (ASFE) and singular value decomposition-based spread transform dither modulation (S-STDM) is proposed in this paper. The proposed ASFE can adaptively extract feature regions from blocks segmented by the simple linear iterative clustering. In addition, the stationary wavelet transform is employed to decompose each of the extracted feature regions because of its shift invariance. Consequently, the novel S-STDM watermarking method is proposed, where we employ singular value decomposition to calculate the approximation coefficients and then select the generated diagonal elements from the decomposed diagonal matrix for watermark message embedding. The experimental results show that the proposed scheme is superior to the existing schemes under various attacks. [ABSTRACT FROM AUTHOR]

In recent years, human action recognition in videos has become an active research topic, being applied in surveillance, security, somatic games, interactive operations, etc. Since most human action recognition systems are designed for PCs, their performance is poor when transplanted to mobile devices. In this paper, we develop a human action recognition system called “RegFrame,” which can rapidly and accurately recognize simple human actions, including 3D actions, on a stand-alone mobile device. The system divides an action recognition process into two steps: object recognition and movement detection. The movement detection is implemented by a novel Nine-Square algorithm that nearly avoids floating point computing, which improves the recognition time. The experimental results show that the proposed “RegFrame” works reliably in different testing scenarios, and it outperforms the action recognition method of the SAMSUNG Galaxy V (S5) by up to 20% in terms of action recognition time. In addition, the proposed system can be flexibly integrated with a variety of applications. [ABSTRACT FROM AUTHOR]