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The Normalized Cut (NCut) model is a popular graph-based model for image segmentation. But it suffers from the excessive normalization problem and weakens the small object and twig segmentation. In this paper, we propose an Explored Normalized Cut (ENCut) model that establishes a balance graph model by adopting a meaningful-loop and a k-step random walk, which reduces the energy of small salient region, so as to enhance the small object segmentation. To improve the twig segmentation, our ENCut model is further enhanced by a new Random Walk Refining Term (RWRT) that adds local attention to our model with the help of an un-supervising random walk. Finally, a move-making based strategy is developed to efficiently solve the ENCut model with RWRT. Experiments on three standard datasets indicate that our model can achieve state-of-the-art results among the NCut-based segmentation models. [ABSTRACT FROM AUTHOR]

Edges are the fundamental visual element for discovering tiny obstacles using a monocular camera. Nevertheless, tiny obstacles often have weak and inconsistent edge cues due to various properties such as small size and similar appearance to the free space, making it hard to capture them. To this end, we propose an occlusion-based multilayer approach, which specifies the scene prior as multilayer regions and utilizes these regions in each obstacle discovery module, i.e., edge detection and proposal extraction. Firstly, an obstacle-aware occlusion edge is generated to accurately capture the obstacle contour by fusing the edge cues inside all the multilayer regions, which intensifies the object characteristics of these obstacles. Then, a multistride sliding window strategy is proposed for capturing proposals that enclose the tiny obstacles as completely as possible. Moreover, a novel obstacle-aware regression model is proposed for effectively discovering obstacles. It is formed by a primary-secondary regressor, which can learn two dissimilarities between obstacles and other categories separately, and eventually generate an obstacle-occupied probability map. The experiments are conducted on two datasets to demonstrate the effectiveness of our approach under different scenarios. And the results show that the proposed method can approximately improve accuracy by 19% over FPHT and PHT, and achieves comparable performance to MergeNet. Furthermore, multiple experiments with different variants validate the contribution of our method. The source code is available at https://github.com/XuefengBUPT/TOD_OMR [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel random walk model, called Dynamic Random Walk (DRW), which adds a new type of dynamic node to the original RW model and reduces redundant calculation by limiting the walk range. To solve the seed-lacking problem of the proposed DRW, we redefine the energy function of the original RW and use the first arrival probability among each node pair to avoid the interference for each partition. Relaxation of our DRW is performed with the help of a greedy strategy and the Weighted Random Walk Entropy(WRWE) that uses the gradient feature to approximate the stationary distribution. The proposed DRW not only can enhance the boundary adherence but also can run with linear time complexity. To extend our DRW for superpixel segmentation, a seed initialization strategy is proposed. It can evenly distribute seeds in both 2D and 3D space and generate superpixels in only one iteration. The experimental results demonstrate that our DRW is faster than existing RW models and better than the state-of-the-art superpixel segmentation algorithms with respect to both efficiency and segmentation effects. [ABSTRACT FROM AUTHOR]

Video content delivery over Internet is receiving increasing attention from both industry and academia, especially for the multiview video contents, as it is the basis to support various applications, such as 3-D video, virtual reality, free view video, and so on. To cope with the dynamic nature of Internet throughput, dynamic adaptive streaming over HTTP (DASH) has been introduced to control the video streaming based on the network conditions. In this paper, we design a streaming framework to improve the user experience of the multiview video streaming over DASH, considering the user behavior of the viewpoint navigation during the streaming process. To eliminate the view switching delay, a multiple view navigation rule is introduced to pre-fetch the possible switching viewpoints. An optimal bitrate allocation scheme is proposed for the introduced rule, allowing the clients to maximize the video quality. Moreover, we found the video quality and the playback starvation probability are conflicting factors, while both are essential for the user’s quality of experience (QoE). To tackle this issue, a QoE optimization solution is designed to maximize the overall performance in the proposed framework. Several experiments verify the effectiveness of the proposed framework, and the results demonstrate that the proposed framework outperforms two typical DASH methods. [ABSTRACT FROM AUTHOR]

In this paper, we first define a reality-preserving multiple parameter fractional angular transform (RPMPDFrAT), which is a useful tool for image encryption. Then, we propose a new color image encryption algorithm based on the defined RPMPDFrAT. The encryption process consists of two phases: encryption in the spatial domain and RPMPDFrAT domain. In the spatial domain, three color components of the plain image are mapped by dual cylindrical transform, which can nonlinearly hide the original color information. Then, the intermediate output is scrambled by a coupled logistic map to reduce the correlation of adjacent pixels and uniformly distribute the image energy of different color components. Thereafter, the scrambled image is transformed by the proposed RPMPDFrAT, which can ensure that we obtain the real-value output. Finally, a process similar to the spatial domain is performed in the RPMPDFrAT domain to further improve the security of the cryptosystem. Numerical simulations are performed and demonstrate that the proposed image encryption algorithm is effective and sensitive to keys. Moreover, some potential attacks are tested to verify the robustness of the proposed method, and the performance of our method outperforms previously published ones. [ABSTRACT FROM AUTHOR]

A depth-ordering reasoning approach first provides novel occlusion edge detection, generating precise same-layer relationship judgment and producing reliable region proposals for the depth-ordering inference. Specifically, a novel sparsity-induced regression model learns a discriminative feature subspace. In addition, kernel ridge regression assigns the occlusion label for each edge. The kernel trick guarantees linearly separable edges in a rich, high-dimensional feature space. Secondly, a couple layers inference approach infers the final depth order. In the semilocal layer, a novel triple descriptor judges the foreground relationship. In the global layer, the inference is executed by finding a valid path on a directed graph model. The proposed approach is validated on the Cornell depth-order and NYU 2 datasets. [ABSTRACT FROM PUBLISHER]