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This letter proposes a new algorithm for solving the optimal zero-forcing beamforming problem that maximizes the user achievable rate with restriction on the per-antenna element power constraints. An accelerated gradient method with step size search is proposed for solving the problem. For each iteration of the gradient approach, a quick one dimensional search is employed to obtain the step size. The advantage of the step-size search is that it is relatively fast and requires only a few calculations of the objective function. Design examples show that the proposed algorithm converges faster than the gradient approach and the accelerated gradient approach with a constant step size while achieving a low computational complexity. [ABSTRACT FROM AUTHOR]

In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted ℓ1 relaxation of the ℓ0 norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches. [ABSTRACT FROM AUTHOR]

When a face is partially occluded in an image, the existing discriminative or generative methods often do not find facial features. This is due to the limitations of local facial feature detectors and appearance modeling in discriminative and generative methods, respectively. To solve this problem, we propose a new facial feature detection method that hybridizes the discriminative and generative methods. The proposed method consists of an initialization stage and optimization stage. The initialization stage detects the face, estimates the facial pose, and obtains the initial parameter set by locating the pose-specific mean shape on the detected face. The optimization stage obtains the facial features by updating the parameter set using the combined Hessian matrix and gradient vector of shape and appearance errors obtained from two methods. Further, we extend the proposed facial feature detection to face tracking by adding a template face obtained from the previous image frame. In experiments, the proposed method yields more accurate facial feature detection or tracking under heavy occlusions and pose variations than the existing methods. [ABSTRACT FROM AUTHOR]

Alternating minimization and steepest descent are commonly used strategies to obtain interference alignment (IA) solutions in the K-user multiple-input multiple-output (MIMO) interference channel (IC). Although these algorithms are shown to converge monotonically, they experience a poor convergence rate, requiring an enormous amount of iterations which substantially increases with the size of the scenario. To alleviate this drawback, in this letter we resort to the Gauss-Newton (GN) method, which is well-known to experience quadratic convergence when the iterates are sufficiently close to the optimum. We discuss the convergence properties of the proposed GN algorithm and provide several numerical examples showing that it always converges to the optimum with quadratic rate, reducing dramatically the required computation time in comparison to other algorithms, hence paving a new way for the design of IA algorithms. [ABSTRACT FROM AUTHOR]

It is an inherently ill-posed problem to separate a single superimposed image into a reflection image and a transmission image. In this letter, a novel algorithm is proposed based on the prior knowledge that edges of weak reflection are always smoother than most edges of observed objects. To filter out the edges of weak reflection, an MRF-EM (Markov Random Field and Expectation Maximization) framework is proposed. In the MRF model, a data energy function is established based on the edge smoothness metric GPS (Gradient Profile Sharpness), and a spatial smoothness energy function is formulated using a weighted Potts model. Moreover, the parameters in the data energy function are updated using the EM algorithm. Experimental results demonstrate that the proposed algorithm can produce superior separation results with less residuals and color distortions compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Action recognition in videos is receiving extensive research interest due to its wide applications. This task needs to assign a specific action class for each video. In this paper, we study the problem of action similarity labeling (ASLAN) that is to verify whether two action videos present the same type of action or not. We show that both Fisher vector (FV) and vector of locally aggregated descriptors (VLAD) with dense trajectory features can achieve state-of-the-art performance on the ASLAN benchmark. Our main contribution is to develop a large margin dimensionality reduction (LMDR) method to compress high-dimensional FV and VLAD. Specially, we leverage the hinge loss objective function and stochastic gradient descent to optimize the discriminative projection matrix of these vectors. Extensive experiments on the ASLAN dataset indicate that our LMDR method not only reduces the dimension significantly but also improves the verification performance. [ABSTRACT FROM AUTHOR]

In this letter, a review of the quaternionic least mean squares (QLMS) algorithm is proposed. Three versions coming from three derivation ways exist: the original QLMS [1] based on componentwise gradients, HR-QLMS [2] based on a quaternion gradient operator and iQLMS [3] based on an involutions-gradient. Noting and investigating the differences between the three QLMS formulations, we show that the original QLMS suffers from a mistake in the derivation calculus. Thus, we propose to derive rigorously the criterion following the first way, giving the correct version of QLMS. A comparison with the other QLMS versions validates these results on simulated data. [ABSTRACT FROM AUTHOR]

Depth map up-sampling methods have achieved remarkable improvement by exploiting sensor fusion techniques where they assume that the depth map discontinuities and image edges coincide, and the depth values of the temporal neighbors are stable during time variation. However, inherent noise of depth data acquired by active range sensors often violates these assumptions, and results in undesirable error propagation. To alleviate the error propagation, this letter presents a new adaptive supporting method of spatially-temporally neighboring samples. On the basis of a spatial-temporal Markov random field model, the weight coefficients of the smoothness terms are adaptively computed according to the reliability of neighboring samples. The experiments show that the proposed method outperforms the previous works in terms of quantitative and qualitative criteria. [ABSTRACT FROM AUTHOR]

In this letter, a novel approach to approximate calculation of Karhunen–Loève transform (KLT) is proposed. It is proved that with the practical assumptions the maximization of the sum of fourth-order moments of random variables in the domain of orthonormal transform leads to any permuted KLT. On the basis of theoretical results, we derive and formulate the gradient method of adaptation of orthonormal parametric transforms. The main qualities of the proposed method are: computational efficiency, high repeatability of results, independence of target processing schemes, an unsupervised adaptation of transform parameters in on-line learning mode based on incoming vectors of input samples. Experimental studies confirm practical effectiveness of the method when applied to adaptation of fast parametric orthonormal transforms. [ABSTRACT FROM PUBLISHER]