The major disadvantage of existing watermarking methods is their limited resistance to extensive geometric attacks. In addition, we have found that the weakness of multiple watermark embedding methods that were initially designed to resist geometric attacks is their inability to withstand the watermark-estimation attacks (WEAs), leading to reduce resistance to geometric attacks. In view of these facts, this paper proposes a robust image watermarking scheme that can withstand geometric distortions and WEAs simultaneously. Our scheme is mainly composed of three components: 1) robust mesh generation and mesh-based watermarking to resist geometric distortions; 2) construction of media hash-based content-dependent watermark to resist WEAs; and 3) a mechanism of false positive-oriented watermark detection, which can be used to determine the existence of a watermark so as to achieve a tradeoff between correct detection and false detection. Furthermore, extensive experimental results obtained using the standard benchmark (i.e., Stirmark) and WEAs, and comparisons with relevant watermarking methods confirm the excellent performance of our method in improving robustness. To our knowledge, such a thorough evaluation has not been reported in the literature before

We propose a violin bowing action recognition system that can accurately recognize distinct bowing actions in classical violin performance. This system can recognize bowing actions by analyzing signals from a depth camera and from inertial sensors that are worn by a violinist. The contribution of this study is threefold: (1) a dataset comprising violin bowing actions was constructed from data captured by a depth camera and multiple inertial sensors; (2) data augmentation was achieved for depth-frame data through rotation in three-dimensional world coordinates and for inertial sensing data through yaw, pitch, and roll angle transformations; and, (3) bowing action classifiers were trained using different modalities, to compensate for the strengths and weaknesses of each modality, based on deep learning methods with a decision-level fusion process. In experiments, large external motions and subtle local motions produced from violin bow manipulations were both accurately recognized by the proposed system (average accuracy > 80%).