KEYWORDS: RGB color model, Computer programming, Binary data, Performance modeling, Data modeling, Convolution, Information fusion, Image fusion, Visualization, Data fusion
In recent years, salient object detection (SOD) in RGB-depth (RGB-D) images has attracted considerable research interest. We present a boundary-enhanced attention-aware network (BANet) for RGB-D SOD by combining boundary and saliency detection networks. In particular, depth maps were used to detect the saliency boundaries effectively, and the corresponding RGB images were used to predict the salient objects. Considering that the data contained in depth maps are insufficient, HSV images were employed as complements to enhance the boundary detection performances. Subsequently, an attention module was used to adaptively weigh the features from the RGB branch and boundary network to improve the SOD performance of the proposed BANet. A loss function combining saliency supervision, background supervision, and boundary supervision was designed to optimize the parameters of the BANet. Extensive experiments were conducted to assess the robustness and effectiveness of the proposed BANet. The results suggest that the proposed BANet shows a significant improvement over other representative SOD approaches.
KEYWORDS: Digital watermarking, Image restoration, Detection and tracking algorithms, Image quality, 3D image processing, Image processing, Image compression, Multimedia, Visualization, Signal to noise ratio
We propose a new watermarking algorithm for stereoscopic image tamper detection and self-recovery in three-dimensional multimedia services. Initially, left and right views of stereoscopic image are divided into nonoverlapping 2×2 blocks in order to improve the accuracy of tamper localization in an image. As the left and right views of a stereoscopic image are not independent from each other but have an inter-view relationship, every block of a stereoscopic image is classified into matching block or nonmatching block and then block disparities are obtained. Both matching blocks in the left and right views have similar pixel values, so that fewer bits are allocated for recovery watermark generation, which can increase the quality of watermarked stereoscopic images. A hierarchical tamper-detection strategy with a four-level checkup is presented to improve the accuracy of tamper localization. Additionally, two copies of block (matching block and nonmatching block) information are embedded into the stereoscopic image, and it assures the quality of tampered recovery. For the nonmatching block recovery, two copies of the partner block are embedded into their chaotic mapping blocks, which supply the second chance for tamper recovery. For the matching block recovery, the inter-view relationship between tampers of left and right views supplies the third chance for tamper recovery. Experimental results show that the proposed algorithm can not only detect and locate tampers in stereoscopic image more accurately but also recover the tampered regions better, compared with other algorithms.
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