Stereoscopic videos have become very popular in recent years. Most of these videos are developed primarily for viewing on large screens located at some distance away from the viewer. If we watch these videos on a small screen located near to us, the depth range of the videos will be seriously reduced, which can significantly degrade the 3D effects of these videos. To address this problem, we propose a linear depth mapping method to adjust the depth range of a stereoscopic video according to the viewing configuration, including pixel density and distance to the screen. Our method tries to minimize the distortion of stereoscopic image contents after depth mapping, by preserving the relationship of neighboring features and preventing line and plane bending. It also considers the depth and motion coherences. While depth coherence ensures smooth changes of the depth field across frames, motion coherence ensures smooth content changes across frames. Our experimental results show that the proposed method can improve the stereoscopic effects while maintaining the quality of the output videos.
This paper devotes to the image compression and encryption problems. We develop a novel hybrid scheme based on block compressive sensing. Concentrate on taking full advantage of the different frequency coefficients sparsity, the nonuniform sampling strategy is adopted to improve the compression efficiency. First, the discrete cosine transform coefficients matrices of blocks are transformed into vectors by zigzag scanning. The different frequency components are extracted in the front, middle, and back of vectors, respectively. Using the measurement matrices with different dimensions, the combination of lowand high-frequency components, together with the medium-frequency coefficients are compressed simultaneously. Second, the recombinational block measurements are re-encrypted by the permutation-diffusion framework. The logistic map is introduced for key stream generation. In order to accomplish a sensitive and effective cryptosystem, the control strategy for secret keys is employed. The simulation results indicate that the proposed scheme forms a high balance between reconstruction performance, storage and computational complexity, and hardware implementation. Moreover, the security analyses demonstrate the satisfactory performance and effectiveness of the proposed cryptosystem. The scheme can work efficiently in the parallel computing environment, especially for the images with medium and large size. INDEX TERMS Block compressive sensing, image cryptosystem, logistic map, nonuniform sampling strategy.
The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods.
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