Iterative Matching Research Articles

Geomagnetic matching navigation algorithm based on robust estimation

The outliers in the geomagnetic survey data seriously affect the precision of the geomagnetic matching navigation and badly disrupt its reliability. A novel algorithm which can eliminate the outliers influence is investigated in this paper. First, the weight function is designed and its principle of the robust estimation is introduced. By combining the relation equation between the matching trajectory and the reference trajectory with the Taylor series expansion for geomagnetic information, a mathematical expression of the longitude, latitude and heading errors is acquired. The robust target function is obtained by the weight function and the mathematical expression. Then the geomagnetic matching problem is converted to the solutions of nonlinear equations. Finally, Newton iteration is applied to implement the novel algorithm. Simulation results show that the matching error of the novel algorithm is decreased to 7.75% compared to the conventional mean square difference (MSD) algorithm, and is decreased to 18.39% to the conventional iterative contour matching algorithm when the outlier is 40nT. Meanwhile, the position error of the novel algorithm is 0.017° while the other two algorithms fail to match when the outlier is 400nT.

Reducing uncertainty in reservoir parameters combining history matching and conditioned geostatistical realizations

History matching is an iterative process that modifies a reservoir model to reproduce field behavior. Due to the scarcity of data the true distribution of facies, porosity and permeability between widely spaced wells is unknown, resulting in high uncertainty.As the spatial patterns of permeability and porosity often significantly affect the flow response, evaluating heterogeneity is key in history matching.This work presents a stochastic method for use in probabilistic and iterative history matching. We select a set of the best geostatistical realizations and reproduce their spatial patterns in subsequent iterations to create a new set of better-matched models. All models, throughout the process, honor well log data and continuity, modeled with the variograms.This paper uses four approaches: two using a global method and two using a regional method. Both methods improved dynamic history data matching while honoring all well data but each is suited to different times in exploration and production.We present the global method as a simple tool to improve models. Characterized as an update of the entire reservoir, it is useful when the number of wells and the dynamic history data are scarce. The regional method is more efficient to process large amounts of information, enabling the independent match of dynamic well data, avoiding mismatches with other wells.

Vimentin fibers orient traction stress.

The intermediate filament vimentin is required for cells to transition from the epithelial state to the mesenchymal state and migrate as single cells; however, little is known about the specific role of vimentin in the regulation of mesenchymal migration. Vimentin is known to have a significantly greater ability to resist stress without breaking in vitro compared with actin or microtubules, and also to increase cell elasticity in vivo. Therefore, we hypothesized that the presence of vimentin could support the anisotropic mechanical strain of single-cell migration. To study this, we fluorescently labeled vimentin with an mEmerald tag using TALEN genome editing. We observed vimentin architecture in migrating human foreskin fibroblasts and found that network organization varied from long, linear bundles, or "fibers," to shorter fragments with a mesh-like organization. We developed image analysis tools employing steerable filtering and iterative graph matching to characterize the fibers embedded in the surrounding mesh. Vimentin fibers were aligned with fibroblast branching and migration direction. The presence of the vimentin network was correlated with 10-fold slower local actin retrograde flow rates, as well as spatial homogenization of actin-based forces transmitted to the substrate. Vimentin fibers coaligned with and were required for the anisotropic orientation of traction stresses. These results indicate that the vimentin network acts as a load-bearing superstructure capable of integrating and reorienting actin-based forces. We propose that vimentin's role in cell motility is to govern the alignment of traction stresses that permit single-cell migration.

A videofluoroscopy-based tracking algorithm for quantifying the time course of human intervertebral displacements

The motions of individual intervertebral joints can affect spine motion, injury risk, deterioration, pain, treatment strategies, and clinical outcomes. Since standard kinematic methods do not provide precise time-course details about individual vertebrae and intervertebral motions, information that could be useful for scientific advancement and clinical assessment, we developed an iterative template matching algorithm to obtain this data from videofluoroscopy images. To assess the bias of our approach, vertebrae in an intact porcine spine were tracked and compared to the motions of high-contrast markers. To estimate precision under clinical conditions, motions of three human cervical spines were tracked independently ten times and vertebral and intervertebral motions associated with individual trials were compared to corresponding averages. Both tests produced errors in intervertebral angular and shear displacements no greater than 0.4° and 0.055 mm, respectively. When applied to two patient cases, aberrant intervertebral motions in the cervical spine were typically found to correlate with patient-specific anatomical features such as disc height loss and osteophytes. The case studies suggest that intervertebral kinematic time-course data could have value in clinical assessments, lead to broader understanding of how specific anatomical features influence joint motions, and in due course inform clinical treatments.

Direct forecasting of reservoir performance using production data without history matching

The conventional paradigm for predicting future reservoir performance from existing production data involves the construction of reservoir models that match the historical data through iterative history matching. This is generally an expensive and difficult task and often results in models that do not accurately assess the uncertainty of the forecast. We propose an alternative re-formulation of the problem, in which the role of the reservoir model is reconsidered. Instead of using the model to match the historical production, and then forecasting, the model is used in combination with Monte Carlo sampling to establish a statistical relationship between the historical and forecast variables. The estimated relationship is then used in conjunction with the actual production data to produce a statistical forecast. This allows quantifying posterior uncertainty on the forecast variable without explicit inversion or history matching. The main rationale behind this is that the reservoir model is highly complex and even so, still remains a simplified representation of the actual subsurface. As statistical relationships can generally only be constructed in low dimensions, compression and dimension reduction of the reservoir models themselves would result in further oversimplification. Conversely, production data and forecast variables are time series data, which are simpler and much more applicable for dimension reduction techniques. We present a dimension reduction approach based on functional data analysis (FDA), and mixed principal component analysis (mixed PCA), followed by canonical correlation analysis (CCA) to maximize the linear correlation between the forecast and production variables. Using these transformed variables, it is then possible to apply linear Gaussian regression and estimate the statistical relationship between the forecast and historical variables. This relationship is used in combination with the actual observed historical data to estimate the posterior distribution of the forecast variable. Sampling from this posterior and reconstructing the corresponding forecast time series, allows assessing uncertainty on the forecast. This workflow will be demonstrated on a case based on a Libyan reservoir and compared with traditional history matching.

Modeling of Ti-6Al-4V machining force considering material microstructure evolution

A physics-based model to predict the force in machining Ti-6Al-4V by accounting for the material dynamic recrystallization under thermal-mechanical loading effects is developed. The behavior of grain-size evolution in the context of mixture rule volume fraction as governed by Johnson-Mehl-Avrami-Kolmogorov (JMAK) is considered. The variation of material flow stress in machining is described by a modified Johnson-Cook flow stress model proposed by considering the grain growth and material softening. The classic Oxley’s cutting mechanics theories are implemented for machining forces calculation based upon iterative stress matching. Accounting for the thermal, mechanical, and microstructure effects in machining, the forces for both orthogonal turning and 3D oblique turning are modeled. The model validation is conducted by comparing the predicted machining forces with experimental measurements.

Thermal-to-Visible Face Alignment on Edge Map

This paper presents a novel thermal-to-visible face alignment method based on edge map. The alignment procedure is inspired by iterative closest points matching. However, iterative closest point (ICP) sometimes converges to the local minimum for edge face alignment. In this paper, pointwise distance, which refers to the linear combination of positional and edge local pattern, is proposed as it gives good initial estimation of the closest points matching in iteration. Another problem is that numerous spurious corresponding points will occur in the closest pairs. Therefore, the selection criteria for partial closest pairs are designed, according to magnitude and orientation of the displacement, to remove the spurious corresponding points caused by edge faces. The alignment performance is evaluated by the bias of facial fiducial points and heterogeneous face recognition. The experimental results, including intra-class and inter-class, are demonstrated to compare partial closest points with local pattern with ICP, manual labeling, and Gaussian fields criterion. The alignment effect of variable expressions and illumination is also tested. Besides, the wearing glasses problem is well solved by glasses replacement.

Social Network-Based Content Delivery in Device-to-Device Underlay Cellular Networks Using Matching Theory

With the popularity of social network-based services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. Device-to-device (D2D) communication, as a promising solution to overcome wireless spectrum crisis, can enable fast content delivery based on user activities in social networks. In this paper, we address the content delivery problem related to optimization of peer discovery and resource allocation by combining both the social and physical layer information in D2D underlay networks. The social relationship, which is modeled as the probability of selecting similar contents and estimated by using the Bayesian nonparametric models, is used as a weight to characterize the impact of social features on D2D pair formation and content sharing. Next, we propose a 3-D iterative matching algorithm to maximize the sum rate of D2D pairs weighted by the intensity of social relationships while guaranteeing the quality of service requirements of both cellular and D2D links simultaneously. Moreover, we prove that the proposed algorithm converges to a stable matching and is weak Pareto optimal, and also provide the theoretical complexity. Simulation results show that the algorithm is able to achieve more than 90% of the optimum performance with a computation complexity 1000 times lower than the exhaustive matching algorithm. It is also demonstrated that the satisfaction performance of D2D receivers can be increased significantly by incorporating social relationships into the resource allocation design.

Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments

We present a practical approach to global motion planning and terrain assessment for ground robots in generic three‐dimensional (3D) environments, including rough outdoor terrain, multilevel facilities, and more complex geometries. Our method computes optimized six‐dimensional trajectories compliant with curvature and continuity constraints directly on unordered point cloud maps, omitting any kind of explicit surface reconstruction, discretization, or topology extraction. We assess terrain geometry and traversability on demand during motion planning, by fitting robot‐sized planar patches to the map and analyzing the local distribution of map points. Our motion planning approach consists of sampling‐based initial trajectory generation, followed by precise local optimization according to a custom cost measure, using a novel, constraint‐aware trajectory optimization paradigm. We embed these methods in a complete autonomous navigation system based on localization and mapping by means of a 3D laser scanner and iterative closest point matching, suitable for both static and dynamic environments. The performance of the planning and terrain assessment algorithms is evaluated in offline experiments using recorded and simulated sensor data. Finally, we present the results of navigation experiments in three different environments—rough outdoor terrain, a two‐level parking garage, and a dynamic environment, demonstrating how the proposed methods enable autonomous navigation in complex 3D terrain.

Robust scene matching method based on sparse representation and iterative correction

This article presents an efficient scene matching method robust to noise and occlusion. The method combines a coarse matching method with a fine matching method by iterative correction. Both coarse matching method and fine matching method, inspired by sparse representation for face recognition, are resistant to noise and occlusion inherently. In each step of iterative matching, the result of coarse matching is introduced into fine matching as prior knowledge, which gives a rough range about the possible positions. Then, the fine matching finds the most reasonable result based on the rough range given by coarse matching. Finally, the result of fine matching is brought back to coarse matching as post knowledge to correct it. Experiments demonstrate that the robustness to noise and occlusion is improved compared with the matching methods without iterative correction.

An automatic recognition and parameter extraction method for structural planes in borehole image

As a breakthrough in borehole imaging technology, digital panoramic borehole camera technology has been widely employed. The high-resolution panoramic borehole images can accurately reproduce the geometric features of structural planes. However, the detection of these features is usually done manually, which is both time-consuming and introduces human errors. To solve this problem, this paper presents a method for the automatic recognition and parameter extraction of borehole geometric features of camera images. In this method, the image's gray and gradient level, and also their projection on the depth axis are used to identify the locations of structural planes. Afterwards, iterative matching is employed by using a template of sinusoidal function to search for structural planes in the identified image blocks. Finally, optimal sine curves are selected as the feature curves of structural planes, and their related parameters are converted into structural plane parameters required for engineering, such as their positions, dip directions, dip angles and fracture widths. The method can automatically identify all of structural planes throughout the whole borehole camera image in a continuous and rapid manner, and obtain the corresponding structural parameters. It has proven highly reliable, accurate and efficient.

Impact of different registration methods in MEG source analysis

Abstract For this study the impact of different co-registration procedures on MEG source localization of somatosensory evoked fields was evaluated. Two different co-registration procedures were used to calculate the transformation matrix which specifies how to align the MRI data to the MEG head coordinate system. In order to depict the differences, caused by the method, the Euclidian distance between the reconstructed sources was noted. It was shown that, erroneous MRI and MEG data co- registration effects source localization results. Most dipoles are located more posterior and superior when the more advanced registration procedure was applied. In conclusion the results show, that an iterative matching procedure allows an accurate knowledge of the MEG gradiometer sensor position relative to the head which is crucial to correctly reconstruct neuronal activity derived from MEG measurements.

IMPROVED REAL-TIME SCAN MATCHING USING CORNER FEATURES

The automation of unmanned vehicle operation has gained a lot of research attention, in the last few years, because of its numerous applications. The vehicle localization is more challenging in indoor environments where absolute positioning measurements (e.g. GPS) are typically unavailable. Laser range finders are among the most widely used sensors that help the unmanned vehicles to localize themselves in indoor environments. Typically, automatic real-time matching of the successive scans is performed either explicitly or implicitly by any localization approach that utilizes laser range finders. Many accustomed approaches such as Iterative Closest Point (ICP), Iterative Matching Range Point (IMRP), Iterative Dual Correspondence (IDC), and Polar Scan Matching (PSM) handles the scan matching problem in an iterative fashion which significantly affects the time consumption. Furthermore, the solution convergence is not guaranteed especially in cases of sharp maneuvers or fast movement. This paper proposes an automated real-time scan matching algorithm where the matching process is initialized using the detected corners. This initialization step aims to increase the convergence probability and to limit the number of iterations needed to reach convergence. The corner detection is preceded by line extraction from the laser scans. To evaluate the probability of line availability in indoor environments, various data sets, offered by different research groups, have been tested and the mean numbers of extracted lines per scan for these data sets are ranging from 4.10 to 8.86 lines of more than 7 points. The set of all intersections between extracted lines are detected as corners regardless of the physical intersection of these line segments in the scan. To account for the uncertainties of the detected corners, the covariance of the corners is estimated using the extracted lines variances. The detected corners are used to estimate the transformation parameters between the successive scan using least squares. These estimated transformation parameters are used to calculate an adjusted initialization for scan matching process. The presented method can be employed solely to match the successive scans and also can be used to aid other accustomed iterative methods to achieve more effective and faster converge. The performance and time consumption of the proposed approach is compared with ICP algorithm alone without initialization in different scenarios such as static period, fast straight movement, and sharp manoeuvers.

IMPROVED REAL-TIME SCAN MATCHING USING CORNER FEATURES

Abstract. The automation of unmanned vehicle operation has gained a lot of research attention, in the last few years, because of its numerous applications. The vehicle localization is more challenging in indoor environments where absolute positioning measurements (e.g. GPS) are typically unavailable. Laser range finders are among the most widely used sensors that help the unmanned vehicles to localize themselves in indoor environments. Typically, automatic real-time matching of the successive scans is performed either explicitly or implicitly by any localization approach that utilizes laser range finders. Many accustomed approaches such as Iterative Closest Point (ICP), Iterative Matching Range Point (IMRP), Iterative Dual Correspondence (IDC), and Polar Scan Matching (PSM) handles the scan matching problem in an iterative fashion which significantly affects the time consumption. Furthermore, the solution convergence is not guaranteed especially in cases of sharp maneuvers or fast movement. This paper proposes an automated real-time scan matching algorithm where the matching process is initialized using the detected corners. This initialization step aims to increase the convergence probability and to limit the number of iterations needed to reach convergence. The corner detection is preceded by line extraction from the laser scans. To evaluate the probability of line availability in indoor environments, various data sets, offered by different research groups, have been tested and the mean numbers of extracted lines per scan for these data sets are ranging from 4.10 to 8.86 lines of more than 7 points. The set of all intersections between extracted lines are detected as corners regardless of the physical intersection of these line segments in the scan. To account for the uncertainties of the detected corners, the covariance of the corners is estimated using the extracted lines variances. The detected corners are used to estimate the transformation parameters between the successive scan using least squares. These estimated transformation parameters are used to calculate an adjusted initialization for scan matching process. The presented method can be employed solely to match the successive scans and also can be used to aid other accustomed iterative methods to achieve more effective and faster converge. The performance and time consumption of the proposed approach is compared with ICP algorithm alone without initialization in different scenarios such as static period, fast straight movement, and sharp manoeuvers.

IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION

This paper introduces a new image-guided non-local dense matching algorithm that focuses on how to solve the following problems: 1) mitigating the influence of vertical parallax to the cost computation in stereo pairs; 2) guaranteeing the performance of dense matching in homogeneous intensity regions with significant disparity changes; 3) limiting the inaccurate cost propagated from depth discontinuity regions; 4) guaranteeing that the path between two pixels in the same region is connected; and 5) defining the cost propagation function between the reliable pixel and the unreliable pixel during disparity interpolation. This paper combines the Census histogram and an improved histogram of oriented gradient (HOG) feature together as the cost metrics, which are then aggregated based on a new iterative non-local matching method and the semi-global matching method. Finally, new rules of cost propagation between the valid pixels and the invalid pixels are defined to improve the disparity interpolation results. The results of our experiments using the benchmarks and the Toronto aerial images from the International Society for Photogrammetry and Remote Sensing (ISPRS) show that the proposed new method can outperform most of the current state-of-the-art stereo dense matching methods.

IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION

Abstract. This paper introduces a new image-guided non-local dense matching algorithm that focuses on how to solve the following problems: 1) mitigating the influence of vertical parallax to the cost computation in stereo pairs; 2) guaranteeing the performance of dense matching in homogeneous intensity regions with significant disparity changes; 3) limiting the inaccurate cost propagated from depth discontinuity regions; 4) guaranteeing that the path between two pixels in the same region is connected; and 5) defining the cost propagation function between the reliable pixel and the unreliable pixel during disparity interpolation. This paper combines the Census histogram and an improved histogram of oriented gradient (HOG) feature together as the cost metrics, which are then aggregated based on a new iterative non-local matching method and the semi-global matching method. Finally, new rules of cost propagation between the valid pixels and the invalid pixels are defined to improve the disparity interpolation results. The results of our experiments using the benchmarks and the Toronto aerial images from the International Society for Photogrammetry and Remote Sensing (ISPRS) show that the proposed new method can outperform most of the current state-of-the-art stereo dense matching methods.

Iterative Energy-Efficient Stable Matching Approach for Context-Aware Resource Allocation in D2D Communications

Energy efficiency (EE) is critical to fully achieve the huge potentials of device-to-device (D2D) communications with limited battery capacity. In this paper, we consider the two-stage EE optimization problem, which consists of a joint spectrum and power allocation problem in the first stage, and a context-aware D2D peer selection problem in the second stage. We provide a general tractable framework for solving the combinatorial problem, which is NP-hard due to the binary and continuous optimization variables. In each stage, user equipments (UEs) from two finite and disjoint sets are matched in a two-sided stable way based on the mutual preferences. First, the preferences of UEs are defined as the maximum achievable EE. An iterative power allocation algorithm is proposed to optimize EE under a specific match, which is developed by exploiting nonlinear fractional programming and Lagrange dual decomposition. Second, we propose an iterative matching algorithm, which first produces a stable match based on the fixed preferences, and then dynamically updates the preferences according to the latest matching results in each iteration. Finally, the properties of the proposed algorithm, including stability, optimality, complexity, and scalability, are analyzed in detail. Numerical results validate the efficiency and superiority of the proposed algorithm under various simulation scenarios.

Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation.

Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight re-estimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed.

Bayesian parametrization of coarse-grain dissipative dynamics models.

We introduce a new bottom-up method for the optimization of dissipative coarse-grain models. The method is based on Bayesian optimization of the likelihood to reproduce a coarse-grained reference trajectory obtained from analysis of a higher resolution molecular dynamics trajectory. This new method is related to force matching techniques, but using the total force on each grain averaged on a coarse time step instead of instantaneous forces. It has the advantage of not being limited to pairwise short-range interactions in the coarse-grain model and also yields an estimation of the friction parameter controlling the dynamics. The theory supporting the method is exposed in a practical perspective, with an analytical solution for the optimal set of parameters. The method was first validated by using it on a system with a known optimum. The new method was then tested on a simple system: n-pentane. The local molecular structure of the optimized model is in excellent agreement with the reference system. An extension of the method allows to get also an excellent agreement for the equilibrium density. As for the dynamic properties, they are also very satisfactory, but more sensitive to the choice of the coarse-grain representation. The quality of the final force field depends on the definition of the coarse grain degrees of freedom and interactions. We consider this method as a serious alternative to other methods like iterative Boltzmann inversion, force matching, and Green-Kubo formulae.

Iterative Pattern Matching using K- nn and Lazy Bayesian Rule

Medical Data is an abundant source of information for research. Enormous quantity of data is available but is put to use in merely diagnostic procedure. Researchers are now spotlighting their interest in the medical field. This paper focuses on the misdiagnosis attribute that is often ignored but may lead to exact cause of disease and thus result in fine diagnosis in the patient.