To drive upgrades of Depth-Image-Based Rendering (DIBR) algorithms, depth image refinement, etc., quality assessment models for DIBR-synthesized images in 3D video systems are developed. However, most of these models could not effectively evaluate distortion due to irregular stretching (e.g., crumbling), which is more complex and common than black holes and regular stretching (e.g., horizontal stretching) in synthesized images. To make an attempt at this issue, a new quality assessment method is proposed for DIBR views. First, feature point matching and affine transformation are adopted to remove and compensate for the global object shift between reference and synthesized view images. Second, multi-scale discrete wavelet transform is utilized to extract multi-scale structure distortion; gradient magnitude similarity is further integrated to highlight the distortion features; morphological open operation and median filtering are adopted to exclude perceptually unimportant features. Third, scores are obtained by standard deviation pooling on distortion feature maps for each wavelet scale and sub-band. Experimental results demonstrate that our proposed model outperforms the state-of-the-art handcrafted feature-based DIBR-synthesized image quality assessment models on IETR database, and performs the best on average on IETR and IRCCyN/IVC databases. The source code will be available athttps://github.com/House-yuyu/DIBR_IQA.

Abstract Incomplete Multi-View Clustering (IMVC) attempts to give an optimal clustering solution for incomplete multi-view data that suffer from missing instances in certain views. However, most existing IMVC methods still have various drawbacks in practical applications, such as arbitrary incomplete scenarios cannot be handled; the computational cost is relatively high; most valuable nonlinear relations among samples are often ignored; complementary information among views is not sufficiently exploited. To address the above issues, in this paper, we present a novel and flexible unified graph learning framework, called Multiple Kernel-based Anchor Graph coupled low-rank Tensor learning for Incomplete Multi-View Clustering (MKAGT_IMVC), whose goal is to adaptively learn the optimal unified similarity matrix from all incomplete views. Specifically, according to the characteristics of incomplete multi-view data, MKAGT_IMVC innovatively improves an anchor selection strategy. Then, a novel cross-view anchor graph fusion mechanism is introduced to construct multiple fused complete anchor graphs, which captures more the intra-view and inter-view nonlinear relations. Moreover, a graph learning model combining low-rank tensor constraint and consensus graph constraint is developed, where all fused complete anchor graphs are regarded as prior knowledge to initialize this model. Extensive experiments conducted on eight incomplete multi-view datasets clearly show that our method delivers superior performance relative to some state-of-the-art methods in terms of clustering ability and time-consuming.

The low-rank representation with adaptive graph regularization (LRAGR) method has been successfully proposed for clustering applications, since it can solve the problems of missing local information and unclear graph structure in data clustering tasks. However, LRAGR utilizes the low-rank representation technique to mine data information, which makes the obtained coefficient matrix too dense and is not conducive to cluster division. Moreover, due to the singular value decomposition, the constraint of the coefficient matrix requires high computational complexity and is difficult to be applied in practical tasks. To address the above issues, in this paper, a new adaptive graph regularization method, called the adaptive graph regularization method based on least squares regression (LSAGR), is proposed for data clustering applications. Specifically, the proposed LSAGR method adopts the Frobenius norm instead of kernel norm to approximate the rank function to satisfy the clustering effect, that is, the coefficients of the cluster-related data are approximately equal. Compared with the traditional LRAGR method, in clustering tasks, the LSAGR method can better reveal the real subspace membership, improve the clustering performance and reduce the computational complexity. Finally, extensive experimental results demonstrate that, compared with several related state-of-the-art methods, the proposed LSAGR method usually has better clustering performance on six real-world image datasets in clustering applications.

Many diseases can be detected via performing the electrocardiogram diagnosis. Although the electrocardiograms can be acquired in the public clinics, hospitals or even at homes by using the existing devices, these existing devices are not tiny enough to carry on the bodies. Hence, it is difficult to acquire the electrocardiograms all the time. In order to acquire the electrocardiograms all the time, this paper proposes a wearable electrocardiogram acquisition system. In particular, the R points of the electrocardiograms are detected by computing the regularities of the signals. If the regularity of a signal at a point v is about -1.5 and it is the minimum around its neighbor, then v is assumed to be the R point. Similarly, the Q points and the R points can be detected accordingly. Here, it is not required to compute the modulus maxima. Hence, the required computational power is very low. The simulation results show that the proposed algorithm outperforms the existing methods in terms of the required computational complexity.

This study proposes an iterative method to approximate an N ‐dimensional optimisation problem with a weighted L p and L 2 norm objective function by a sequence of N independent one‐dimensional optimisation problems. Inspired by the existing weighted L 1 and L 2 norm separable surrogate functional (SSF) iterative shrinkage algorithm, there are N independent one‐dimensional optimisation problems with weighted L p and L 2 norm objective functions. However, these optimisation problems are non‐convex. Hence, they may have more than one locally optimal solutions and it is very difficult to find their globally optimal solutions. This paper proposes to partition the feasible set of each approximated problem into various regions such that the sign of the convexity of the objective function in each region remains unchanged. Here, there is no more than one stationary point in each region. By finding the stationary point in each region, the globally optimal solution of each approximated optimisation problem can be found. Besides, this study also shows that the sequence of the globally optimal solutions of the approximated problems converge to the globally optimal solution of the original optimisation problem. Computer numerical simulation results show that the proposed method outperforms the existing weighted L 1 and L 2 norm SSF iterative shrinkage algorithm.

The main contribution of this paper is to propose an optimal joint design of an orthonormal real valued short time block code and a linear transceiver for multi-input multi-output (MIMO) wireless digital communication systems in next generation home. Firstly, a relaxed zero forcing condition governing the relationship between the short time block code and the linear transceiver is imposed via a Karhunen Loève transform (KLT) approach. The relaxed zero forcing condition guarantees that there is no transmission error under a noise free environment. Secondly, the linear transceiver is optimally designed via the orthogonal Procrustes approach. In particular, the transmission power gain is minimised subject to a specification on the ratio of the signal gain to the noise gain as well as to the relaxed zero forcing condition. Computer numerical simulation results show that our proposed optimal joint design of the orthonormal real valued short time block code and the linear transceiver can significantly improve the performances of MIMO wireless digital communication systems in next generation home.

Maximum a-posteriori (MAP) inference in Markov random fields (MRF) is an important topic in machine learning, computer vision and other fields. Message passing algorithms based on linear programming (LP) relaxation are powerful tools for the MAP-MRF problems. However, current message passing algorithms are usually based on simple subgraphs, resulting in slow convergence, local optimum and untightness of the LP relaxation for many problems. By extending the junction tree representation, we propose a general convergent message passing algorithm, which can work on arbitrary tractable bounded treewidth subgraphs. In the extended junction tree representation, the minimization and summation operators are commutable so that the proposed algorithm based on the extended junction tree is guaranteed to converge. Based on the treewidth-2 decomposition, better performance of the proposed algorithm is demonstrated on stereo matching, optical flow and panorama.