Visual object tracking, which aims to estimate the position of an arbitrary target in a video sequence automatically, has drawn great attention in recent years. Many efforts have been made regarding this topic. The Siamese network, with a balanced accuracy and speed, has achieved great success. The Siamese network consists of two branches: one for the target image and the other for the search image. The position with the maximum score in the similarity map between the target and the search images indicates the place of the target image in the search image. Current Siamese trackers treat the features of different channels and spatial locations equally. However, the features of different channels and spatial locations may represent different semantic information. We propose a channel and spatial (CS) attention-based Siamese network for visual object tracking. A CS attention mechanism is inserted into the feature extractor to enhance the semantic feature learning. The experimental results show that the proposed network significantly improves the performance of the baseline tracker and is one of the top-ranked trackers among all tested state-of-the-art trackers on the most widely used visual object tracking datasets.
A gray-level intensity image can be employed as a host image for hiding a watermark image to protect information security. Past research works demonstrate that a gray-level hidden image can be embedded into the host image by a digital phase-only holography method. However, the fidelity of retrieved watermark image from the host image is not very satisfactory and the host image quality is degraded due to the insertion of external data bits, especially when observers focus on the saliency regions in the host image. To address this problem, we propose a steganography method based on digital holography and the saliency map of the host image. First, we calculate the hidden capacity (number of bits to be replaced) for each host image pixel based on the weighted sum of pixel intensity and saliency value. Next, a multilevel phase-only digital hologram of the watermark image will be calculated by the Gerchberg–Saxton method under the constraint of the hidden capacity of host image. Finally, we embedded the multilevel phase-only digital hologram into the host image by replacing a corresponding number of bits in each pixel. In this way, the host image can preserve good image fidelity for its saliency regions even if we hide a large amount of digital hologram data into the host image. The experimental results show that the quality of retrieved watermark image from the host image and the quality of saliency regions in the watermarked host image, in our proposed scheme, are superior to the state-of-the-art works reported.
In this paper, two novel hologram image processing issues, i.e., hologram decomposition and hologram inpainting, are briefly reviewed and discussed. By hologram decomposition, one hologram can be decomposed into several subholograms and each sub-hologram represents one individual item in the 3D object scene. A Virtual Diffraction Plane based hologram decomposition scheme is proposed based on Otsu thresholding segmentation, morphological dilation and sequential scan labelling. Hologram decomposition can be employed for focus distance detection in blind hologram reconstruction. By hologram impainting, a damaged hologram can be restored by filling in the missing pixels. An exemplar and search based technique is applied for hologram inpainting with enhanced computing speed by Artificial Bee Colony algorithm. Potential applications of hologram inpainting are discussed.
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