When wireless hosts use different rates to transmit data in IEEE 802.11 networks, it will take on the state of performance anomaly which will severely decrease the throughputs of all the higher rate hosts. Hence, it i...
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When wireless hosts use different rates to transmit data in IEEE 802.11 networks, it will take on the state of performance anomaly which will severely decrease the throughputs of all the higher rate hosts. Hence, it is bad for video service transmission. Considering that video is very sensitive to packet delivery delay but can tolerate some packet losses, we propose a novel cross-layer scheme which takes these two characteristics into consideration. Firstly, the maximum number of retransmissions for a video Medium Access Control (MAC) frame is computed in MAC layer according to video frame rate requirement of application layer and current access delay of MAC layer. Secondly, within the margin of the tolerant Packet Loss Rate (PLR) of application layer, several video MAC frames are allowed to drop so that we can adaptively select the transmission rate as high as possible for the rest of video MAC frames in terms of current channel quality and the maximum number of retransmissions. Experiment results show that the proposed method can reduce the delay and jitter of video service and improve the throughputs of fast hosts. Therefore, it increases the quality of reconstructed video to a certain extent and relieves the performance anomaly of network effectively.
For the special different nature images, we could hardly find particularly desirable approach, and there always exist Gibbs-type artifacts in the results of most methods. A novel Partial Differential Equation (PDE) mo...
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In this paper, a novel approach is proposed for unsupervised change detection of multitemporal remote sensing images. The proposed method is able to produce the change detection result on the difference image without ...
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This paper proposes a robust piecewise planar multi view stereo (MVS) approach specifically designed for urban scenes. These architectural scenes are problematic for traditional computer vision methods. In our work, w...
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This paper proposes a robust piecewise planar multi view stereo (MVS) approach specifically designed for urban scenes. These architectural scenes are problematic for traditional computer vision methods. In our work, we focus on exploiting some useful constraints of artificial structures such as piecewise coplanarity and boundaries of superpixels. Firstly, we reconstruct quasi-dense 3D point clouds of urban scenes using patches-based MVS (PMVS) method. Secondly, a set of 3D candidate planes are generated by the obtained point clouds without any assumption on the normals of planes, unlike famous Manhattan-world assumption. Then, we segment multi-view images with watershed algorithm and modify the contours of superpixels by the classical Douglas-Peucker approximation algorithm to fit the contours to the boundaries of objects in urban scenes as much as possible. Finally, we use the candidate planes as labels and superpixels as nodes to formulate our Markov Random Field (MRF) optimization problem, then a piecewise planar depth map for each view is recovered by solving the optimization problem using graph-cuts. Experiments show that our method outperforms previous approaches in terms of accuracy.
Nonparametric Dirichlet Process Mixtures (MDP) model algorithm is applied to segment images, which can obtain the segmentation class numbers automatically without initialization. The algorithm is used to segment noisy...
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Nonparametric Dirichlet Process Mixtures (MDP) model algorithm is applied to segment images, which can obtain the segmentation class numbers automatically without initialization. In this paper a modified Dirichlet pro...
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When people take photos through a transparent surface, it is ubiquitous that the images are superimposed with two source images, the transmitted layer and the reflected layer. In this paper, we utilize the sparsity pr...
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When people take photos through a transparent surface, it is ubiquitous that the images are superimposed with two source images, the transmitted layer and the reflected layer. In this paper, we utilize the sparsity priors over image color distribution and image gradients, and we realize the separation of such images by exploiting the diversity (relative motions of source images) in different snapshots. Our approach can estimate the motion even when layer image is quite faint, extract layer gradients when layer intensity is unchanged, and obtain a reliable separation by considering the gradients as limitations. The effectiveness of our approach is shown in experiments on both synthetic images and real world photos.
Background subtraction is a basic task for many computer vision applications, yet in dynamic scenes it is still a challenging problem. In this paper, we propose a new method to deal with this difficulty. Our approach ...
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Background subtraction is a basic task for many computer vision applications, yet in dynamic scenes it is still a challenging problem. In this paper, we propose a new method to deal with this difficulty. Our approach is based on robust linear regression model and casts background subtraction as a outlier signal estimation problem. In our linear regression model, we explicitly model the error term as a combination of two components: foreground outlier and background noise. The foreground outlier is sparse and can be arbitrarily large in most cases, while the background noise is relatively small and dispersed. In order to reliably estimate the coefficients under the constraint of sparse foreground outlier, we propose a new objective function. Then we transform the function to fit our problem by only estimating the foreground outlier and give the solution method. Experimental results demonstrate the effectiveness of our method.
Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices - spiking neural P systems (for short, SN P systems). However, the binary...
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This paper proposes a novel method to locate crowd behavior instability spatio-temporally using a velocity-field based social force model. Considering the impacts of velocity field on interaction force between individ...
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This paper proposes a novel method to locate crowd behavior instability spatio-temporally using a velocity-field based social force model. Considering the impacts of velocity field on interaction force between individuals, we establish an improved social force model by introducing collision probability in view of velocity distribution. As compared with commonly- used social force model, which defines interaction force as a dependent variable of relative geometric (physical) position of the individuals, this improved model can provide a better prediction of interactions using the collision probability in a dynamic crowd. With spatio-temporal instability analysis, we can extract video clips with potential abnormality and as well locate region of interest where abnormality is likely to happen. The experimental results demonstrate that the proposed method can be applied to detection of abnormal events with high accuracy of instability estimation due to the velocity-field based social force model.
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