Efficient depth map estimation method based on gradient weight cost aggregation strategy

Video sensor networking technologies have developed very rapidly in the last ten years. In this paper, a cross-based framework strategy for cost aggregation is presented for the depth map estimation based on video sensor networks. We formulate the process as a local regression problem consisting of two main steps with a pair of video sensors. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4%, and 8.3%, when compared with adaptive support weight (ASW) algorithm. Comparing to cross-based algorithm, the proposed algorithm gives 2.0%, 1.3%, and 1.0% in terms of nonocclusion, all, and discontinuities, respectively.

Adaptive support weight (ASW) strategies in local stereo matching have recently attracted many researchers due to their compelling results. In this paper, we present an evaluation study that focuses on weight computation methods that have been suggested in the most recent literature. We implemented 9 ASW stereo methods and tested them on all (35) ground truth test stereo image pairs of the Middlebury benchmark. Our evaluation considers both the accuracy of the matching process and the computational efficiency of its GPU implementation. According to our results, high-quality matching results at real-time processing speeds can be achieved by using the guided image filter weights.

This paper presents a new local binocular stereo matching algorithm. A local window with adaptive support weights is used to aggregate matching costs. A more robust matching score is set up combining sum of absolute intensity differences and a gradient based measure. Each pixel has a different disparity search range according to disparity gradient analysis and computational complexity decreases rapidly. Experimental results using the Middlebury stereo test bed demonstrate the validity of our presented approach.

基于颜色内相关和自适应支撑权重的立体匹配算法

Enhanced handoff scheme based on efficient uplink quality estimation in LTE-Advanced system

Many diagnostic MRI sequences demand reliable and uniform fat suppression. Multipoint water-fat separation methods, which are based on chemical-shift induced phase differences, have shown great success in the presence of field inhomogeneities. This work presents a computationally efficient and robust field map estimation method. The method begins with subsampling image data into a multiresolution image pyramidal structure, and then utilizes a golden section search to directly locate possible field map values at the coarsest level of the pyramidal structure. The field map estimate is refined and propagated to increasingly finer resolutions in an efficient manner until the full-resolution field map is obtained for final water-fat separation. The proposed method is validated with multiecho sequences where long echo-spacings normally impose great challenges on reliable field map estimation.

We consider the determination of three-dimensional maps of received signal strength (3D-RSS maps) for disaster-recovery networks enabled by unmanned aerial vehicles (UAVs). In this paper, we extend the existing tensor completion based estimator to propose an efficient new 3D-RSS map estimator. To reduce the sensing route length for the UAV, the proposed method utilizes two approaches for estimating the RSS maps (the tensor completion-based and path-loss-based approaches), depending upon the number of high buildings. We show by simulation experiments that the proposed method can achieve a data-collection time comparable to those of existing methods with a shorter sensing route.

Relative velocity estimation is a key challenge for moving target detection in current forward-looking scanning radar-aerial collision avoidance systems. Since the conventional methods suffer from low precision or heavy computation load for fast-moving target motion estimation, this paper proposes an accurate and efficient two-step estimation method that improves the efficiency and accuracy. To improve the estimation efficiency, the first step utilizes Radon transform to narrow the velocity interval which reduces the number of searches. The second step is devoted to fine searching the velocity interval by the minimum entropy criterion, which ensures high estimation accuracy. In the search process, a two-point interpolation method is proposed to reduce the search time and further improve the estimation efficiency. Theoretical and simulation experimental analyses provide qualitative and quantitative evaluations of the efficiency and accuracy of the proposed method.

Beside the wide success of discrete time models in system identification, some advantages of continuous time models have also been acknowledged, in particular, the ability of fully benefiting from fast sampling devices. This paper proposes a continuous time black-box model structure for nonlinear system identification, together with an efficient model estimation method. This model structure belongs to the class of continuous time nonlinear ARX (AutoRegressive with eXogenous input) models, with the particularity of being integrable. By applying techniques of adaptive observer, models of the proposed structure can be efficiently estimated from input-output data, without requiring computing the time derivatives of the data. The proposed model structure and estimation method are illustrated through the identification of a magneto-rheological fluid damper system.

State estimation plays a vital role in UAV navigation and control. With the continuous decrease in sensor cost and size, UAVs equipped with multiple LiDARs, Inertial Measurement Units (IMUs), and cameras have attracted increasing attention. Such systems can acquire rich environmental and motion information from multiple perspectives, thereby enabling more precise navigation and mapping in complex environments. However, efficiently utilizing multi-sensor data for state estimation remains challenging. There is a complex coupling relationship between IMUs’ bias and UAV state. To address these challenges, this paper proposes an efficient and accurate UAV state estimation method tailored for multi-LiDAR–IMU–camera systems. Specifically, we first construct an efficient distributed state estimation model. It decomposes the multi-LiDAR–IMU–camera system into a series of single LiDAR–IMU–camera subsystems, reformulating the complex coupling problem as an efficient distributed state estimation problem. Then, we derive an accurate feedback function to constrain and optimize the UAV state using estimated subsystem states, thus enhancing overall estimation accuracy. Based on this model, we design an efficient distributed state estimation algorithm with multi-LiDAR-IMU-Camerafusion, termed DLIC. DLIC achieves robust multi-sensor data fusion via shared feature maps, effectively improving both estimation robustness and accuracy. In addition, we design an accelerated image-to-point cloud registration module (A-I2P) to provide reliable visual measurements, further boosting state estimation efficiency. Extensive experiments are conducted on 18 real-world indoor and outdoor scenarios from the public NTU VIRAL dataset. The results demonstrate that DLIC consistently outperforms existing multi-sensor methods across key evaluation metrics, including RMSE, MAE, SD, and SSE. More importantly, our method runs in real time on a resource-constrained embedded device equipped with only an 8-core CPU, while maintaining low memory consumption.

Due to the epistemic uncertainties involved in the numerical values of its parameters and its modeling, the system may not accomplish its function as required. Functional failure becomes an important factors in the natural circulation system operation failure, which needs to be considered in the system reliability analysis. In order to solve the problem of multi-dimensional uncertainty parameters and small functional failure probability of passive systems, an efficient functional reliability estimation method based on improved response surface and importance sampling subset simulation was presented. This method was applied in natural circulation cooling in XAPR reactor core. Combined with MLOCA, the uncertainties related to the input parameters and the model were considered. And then the probability of functional failure and the sensitivity measures were estimated with the combination of response surface method and importance sampling subset simulation method. The assessment results show that the functional failure probability is 3.796 × 10−3, and it is fully considered the functional failure in the passive system reliability analysis. Meanwhile, this method has high computed efficiency and excellent computed accuracy compared with traditional probability analysis methods, and it is robust to nonlinear functional function of XAPR.

UK regulatory price reviews and the role of efficiency estimates

Target direction-of-arrival (DOA) estimation is often difficult in the presence of strong interference-especially when the target DOA is very close to the interference DOA-since the strong interference signal can mask the weak target signal and make the DOA estimation hard. To address this problem, an efficient sparse method for DOA estimation is proposed in this paper, in which the effect of strong interference on the target DOA estimation is significantly reduced. An on-grid version of the grid evolution technique is then developed to nonuniformly refine the grid, thereby reducing the computational complexity while retaining reasonable accuracy. Numerical simulations and experimental results demonstrate that, compared to state-of-art methods, the proposed method achieves higher efficiency and better DOA estimation performance in the presence of strong interference.

In this paper, we present an external calibration technique for typical multi-camera system. The technique is very handy in practice using a simple planar pattern. Based on homography, an efficient pair-wise estimation method is proposed to recover the rigid rotation and translation between neighboring cameras. By registering all these partial calibrated structures, complete calibration of the multi-camera system is accomplished. Experiments with both simulated and real data show that accurate and stable calibration results can be achieved by the proposed method.KeywordsCamera calibrationmulti-camera systemhomography

Case-cohort (Prentice, 1986) and nested case-control (Thomas, 1977) designs have been widely used as a cost-effective alternative to the full-cohort design. In this article, we propose an efficient likelihood-based estimation method for the accelerated failure time model under case-cohort and nested case-control designs. An EM algorithm is developed to maximize the likelihood function and a kernel smoothing technique is adopted to facilitate the estimation in the M-step of the EM algorithm. We show that the proposed estimators for the regression coefficients are consistent and asymptotically normal. The asymptotic variance of the estimators can be consistently estimated using an EM-aided numerical differentiation method. Simulation studies are conducted to evaluate the finite-sample performance of the estimators and an application to a Wilms tumor data set is also given to illustrate the methodology.