Set Of Control Points Research Articles

APPROACH FOR IMPROVING THE INTEGRATED SENSOR ORIENTATION

The direct determination of exterior orientation parameters (EOP) of aerial images via integration of the Inertial Measurement Unit (IMU) and GPS is often used in photogrammetric mapping nowadays. The accuracies of the EOP depend on the accurate parameters related to sensors mounting when the job is performed (offsets of the IMU relative to the projection centre and the angles of boresigth misalignment between the IMU and the photogrammetric coordinate system). In principle, when the EOP values do not achieve the required accuracies for the photogrammetric application, the approach, known as Integrated Sensor Orientation (ISO), is used to refine the direct EOP. ISO approach requires accurate Interior Orientation Parameters (IOP) and standard deviation of the EOP under flight condition. This paper investigates the feasibility of use the <i>in situ</i> camera calibration to obtain these requirements. The camera calibration uses a small sub block of images, extracted from the entire block. A digital Vexcel UltraCam XP camera connected to APPLANIX POS AV<sup>TM</sup> system was used to get two small blocks of images that were use in this study. The blocks have different flight heights and opposite flight directions. The proposed methodology improved significantly the vertical and horizontal accuracies of the 3D point intersection. Using a minimum set of control points, the horizontal and vertical accuracies achieved nearly one image pixel of resolution on the ground (GSD). The experimental results are shown and discussed.

Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images.

Accurate preoperative planning is crucial for the outcome of total hip arthroplasty. Recently, 2D pelvic X-ray radiographs have been replaced by 3D CT. However, CT suffers from relatively high radiation dosage and cost. An alternative is to reconstruct a 3D patient-specific volume data from 2D X-ray images. In this paper, based on a fully automatic image segmentation algorithm, we propose a new control point-based 2D-3D registration approach for a deformable registration of a 3D volumetric template to a limited number of 2D calibrated X-ray images and show its application to personalized reconstruction of 3D volumes of the proximal femur. The 2D-3D registration is done with a hierarchical two-stage strategy: the scaled-rigid 2D-3D registration stage followed by a regularized deformable B-spline 2D-3D registration stage. In both stages, a set of control points with uniform spacing are placed over the domain of the 3D volumetric template first. The registration is then driven by computing updated positions of these control points with intensity-based 2D-2D image registrations of the input X-ray images with the associated digitally reconstructed radiographs, which allows computing the associated registration transformation at each stage. Evaluated on datasets of 44 patients, our method achieved an overall surface reconstruction accuracy of [Formula: see text] and an average Dice coefficient of [Formula: see text]. We further investigated the cortical bone region reconstruction accuracy, which is important for planning cementless total hip arthroplasty. An average cortical bone region Dice coefficient of [Formula: see text] and an inner cortical bone surface reconstruction accuracy of [Formula: see text] were found. In summary, we developed a new approach for reconstruction of 3D personalized volumes of the proximal femur from 2D X-ray images. Comprehensive experiments demonstrated the efficacy of the present approach.

Automatic Deformity Estimation for Thoracic Section Between Inhale and Exhale Positions

Corresponding control point pairs or landmarks in images can be used to define the deformation with respect to time, point of view or modality. Manual definition of the number of control points in an image, enough to define all kinds of deformation is a tedious task. Hence, automatic definition of control points is the way forward taken in this proposed work. The paper proposes an automatic registration process for tracing of the deformity path of the thoracic region based on feature detector speeded up robust feature (SURF) and moving least squares (MLS). The set of control points on the images is defined by its feature set which is obtained by using the SURF detector, which serves as input for MLS algorithm to trace the deformations of the image (thoracic image in this case). The credibility and performance of the above proposed method is demonstrated by its outstanding experimental results.

Assembly design in aeronautic field: From assembly jigs to tolerance analysis

Tolerance analysis represents the best way to solve assembly problems in order to improve the quality and to reduce the costs. It is a critical step to design and to build a product such as an aircraft and its importance is grown up in the past years. This work presents a method for the tolerance analysis of an assembly involving free-form surfaces with large dimensions. The assembly is a tail beam, a structural component of an aircraft that is constituted by five parts of large dimensions. The influence of the tolerances applied to the five components of the tail beam on the value of the gap at the interfaces among the parts has been deeply investigated. A set of control points have been distributed on the free-form surfaces of the tail beam; its number and its distribution have been opportunely designed. Moreover, the influence of the tolerances on the other requirements of the tail beam connected with the motion drive has been studied. Tolerance analysis has involved the choice of the assembly tools and sequence too. The assembly jigs are mated with the assembly components through pins that are inserted into tooling holes located on the assembly components. The fit conditions have been modeled and the tolerances of the tooling hole have been opportunely chosen. Each tolerance of the tail beam components has been modeled by means of a probability density function. Monte Carlo approach has been used to obtain the statistical distribution of the assembly requirements, once dimensions and geometry of the tail beam components have been perturbed inside the tolerance ranges. Monte Carlo simulation has been carried out by a well-known computer-aided tolerance software, eM-Tolmate of UGS®.

How does forest structure affect root reinforcement and susceptibility to shallow landslides?

AbstractForests can decrease the risk of shallow landslides by mechanically reinforcing the soil and positively influencing its water balance. However, little is known about the effect of different forest structures on slope stability. In the study area in St Antönien, Switzerland, we applied statistical prediction models and a physically‐based model for spatial distribution of root reinforcement in order to quantify the influence of forest structure on slope stability. We designed a generalized linear regression model and a random forest model including variables describing forest structure along with terrain parameters for a set of landslide and control points facing similar slope angle and tree coverage. The root distribution measured at regular distances from seven trees in the same study area was used to calibrate a root distribution model. The root reinforcement was calculated as a function of tree dimension and distance from tree with the root bundle model (RBMw). Based on the modelled values of root reinforcement, we introduced a proxy‐variable for root reinforcement of the nearest tree using a gamma distribution. The results of the statistical analysis show that variables related to forest structure significantly influence landslide susceptibility along with terrain parameters. Significant effects were found for gap length, the distance to the nearest trees and the proxy‐variable for root reinforcement of the nearest tree. Gaps longer than 20 m critically increased the susceptibility to landslides. Root reinforcement decreased with increasing distance from trees and is smaller in landslide plots compared to control plots. Furthermore, the influence of forest structure strongly depends on geomorphological and hydrological conditions. Our results enhance the quantitative knowledge about the influence of forest structure on root reinforcement and landslide susceptibility and support existing management recommendations for protection against gravitational natural hazards. Copyright © 2015 John Wiley & Sons, Ltd.

Computation and visualisation of the accuracy of old maps using differential distortion analysis

ABSTRACTThe accuracy of old maps can hold interesting historical information, and is therefore studied using distortion analysis methods. These methods start from a set of ground control points that are identified both on the old map and on a modern reference map or globe, and conclude with techniques that compute and visualise distortion. Such techniques have advanced over the years, but leave room for improvement, as the current ones result in approximate values and a coarse spatial resolution. We propose a more elegant and more accurate way to compute distortion of old maps by translating the technique of differential distortion analysis, used in map projection theory, to the setting where an old map and a reference map are directly compared. This enables the application of various useful distortion metrics to the study of old maps, such as the area scale factor, the maximum angular distortion and the Tissot indicatrices. As such a technique is always embedded in a full distortion analysis method we start by putting forward an optimal analysis method for a general-purpose study, which then serves as the foundation for the development of our technique. Thereto, we discuss the structure of distortion analysis methods and the various options available for every step of the process, including the different settings in which the old map can be compared to its modern counterpart, the techniques that can be used to interpolate between both, and the techniques available to compute and visualise the distortion. We conclude by applying our general-purpose method, including the differential distortion analysis technique, to an example map also used in other literature.

Attitude Refinement for Orbiting Pushbroom Cameras: a Simple Polynomial Fitting Method

This paper describes a simple pushbroom camera model for Earth observation satellites and proposes a new algorithm to rene the orientation parameters of a camera from a set of ground control points. The relative importance of the various orientation parameters is analyzed. On the last generation of high resolution satellites such as Pl eiades and WorldView, the attitude angles are shown to be the main contributors to localization errors. Thus the proposed algorithm focuses on rening the attitude angles. It is based on a simple polynomial tting method. Numerous experiments, which can be reproduced through the online demo associated to this paper, show that the proposed algorithm is able to reduce the localization error by one order of magnitude with only a few ground control points. A geometric simulator for the proposed model is implemented, as well as the attitude renement algorithm.

Cramér-Rao Lower Bound for Point Based Image Registration With Heteroscedastic Error Model for Application in Single Molecule Microscopy.

The Cramér-Rao lower bound for the estimation of the affine transformation parameters in a multivariate heteroscedastic errors-in-variables model is derived. The model is suitable for feature-based image registration in which both sets of control points are localized with errors whose covariance matrices vary from point to point. With focus given to the registration of fluorescence microscopy images, the Cramér-Rao lower bound for the estimation of a feature's position (e.g., of a single molecule) in a registered image is also derived. In the particular case where all covariance matrices for the localization errors are scalar multiples of a common positive definite matrix (e.g., the identity matrix), as can be assumed in fluorescence microscopy, then simplified expressions for the Cramér-Rao lower bound are given. Under certain simplifying assumptions these expressions are shown to match asymptotic distributions for a previously presented set of estimators. Theoretical results are verified with simulations and experimental data.

Three-dimensional simulations of the cell growth and cytokinesis using the immersed boundary method

In this paper, we present a three-dimensional immersed boundary method to simulate the eukaryotic cell growth and cytokinesis. The proposed model and numerical method are a non-trivial three-dimensional extension of the previous work (Li et al., 2012). Unstructured triangular meshes are employed to discretize the cell membrane. The nodes of the surface mesh constitute a set of Lagrangian control points used to track the motion of the cell. A surface remeshing algorithm is applied to prevent mesh distortion during evolution. We also use a volume-conserving algorithm to maintain the mass of cells in cytokinesis. The ability of the proposed method to simulate cell growth and division processes is numerically demonstrated.

A 3D reconstruction method of the body envelope from biplanar X-rays: Evaluation of its accuracy and reliability

The aim of this study was to propose a novel method for reconstructing the external body envelope from the low dose biplanar X-rays of a person. The 3D body envelope was obtained by deforming a template to match the surface profiles in two X-rays images in three successive steps: global morphing to adopt the position of a person and scale the template׳s body segments, followed by a gross deformation and a fine deformation using two sets of pre-defined control points. To evaluate the method, a biplanar X-ray acquisition was obtained from head to foot for 12 volunteers in a standing posture. Up to 172 radio-opaque skin markers were attached to the body surface and used as reference positions. Each envelope was reconstructed three times by three operators. Results showed a bias lower than 7mm and a confidence interval (95%) of reproducibility lower than 6mm for all body parts, comparable to other existing methods matching a template onto stereographic photographs. The proposed method offers the possibility of reconstructing body shape in addition to the skeleton using a low dose biplanar X-rays system.

A New Approach for Combining Time-of-Flight and RGB Cameras Based on Depth-Dependent Planar Projective Transformations.

Image registration for sensor fusion is a valuable technique to acquire 3D and colour information for a scene. Nevertheless, this process normally relies on feature-matching techniques, which is a drawback for combining sensors that are not able to deliver common features. The combination of ToF and RGB cameras is an instance that problem. Typically, the fusion of these sensors is based on the extrinsic parameter computation of the coordinate transformation between the two cameras. This leads to a loss of colour information because of the low resolution of the ToF camera, and sophisticated algorithms are required to minimize this issue. This work proposes a method for sensor registration with non-common features and that avoids the loss of colour information. The depth information is used as a virtual feature for estimating a depth-dependent homography lookup table (Hlut). The homographies are computed within sets of ground control points of 104 images. Since the distance from the control points to the ToF camera are known, the working distance of each element on the Hlut is estimated. Finally, two series of experimental tests have been carried out in order to validate the capabilities of the proposed method.

Image Segmentation With Cage Active Contours.

In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods.

A jitter compensation method for spaceborne line-array imagery using compressive sampling

Attitude jitter is a problem commonly experienced by remote-sensing satellites, and deteriorates the accuracy of geo-positioning and mapping. Therefore, it is important to design a method which can handle this problem. This paper presents a compensation method using compressive sampling. In this method, the influence of attitude jitter in imagery is represented by two fluctuating signals, which are called distortion signals in this paper. Using compressive sampling, the distortion signals in the remote-sensing imagery can be estimated with a small set of ground control points (GCPs). Based on the estimated distortion signals, we can conduct compensation on the remote-sensing imagery affected by attitude jitter. The significant advantage of this method is that it requires only rational function model (RFM) and few GCPs, which makes it easier to be applied than other similar methods. Experiment with image from satellite ALOS (Advanced Land Observing Satellite) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiaometer) showed the feasibility of the proposed method and the effect of the distribution of GCPs.

Geometric correction method to correct the influence of attitude jitter on remote sensing imagery using compressive sampling

Attitude jitter occurs widely in the applications of high-resolution satellites. It is a vital factor that deteriorates the accuracy of geopositioning and mapping. However, the normal geometric correction methods cannot eliminate the influence of jitter on remote sensing images. Therefore, it is important to design a method that can handle this problem. This paper presents a geometric correction method using a rational function model (RFM) and compressive sampling called RFM-CS. This method is divided into two modules: precorrection and compensation. In the precorrection part, the original image is geometrically corrected with an RFM. However, when raw images contain distortion caused by attitude jitter, the rational polynomial coefficients (RPCs) don’t approximate the real imaging process well, and there are significant residual distortions in precorrected images. In the compensation part, we propose a new idea by which the residual distortions of images can be expressed as two-dimensional signals, which are called distortion signals. Using the new idea and CS, distortion signals, even those containing the influence of attitude jitter, are exactly reconstructed with a small set of ground control points. Based on the reconstructed distortion signals, the residual geometric error in the remote sensing images can be compensated by resampling. The experiments with images from Advanced Spaceborne Thermal Emission and Reflection Radiometer, Advanced Land Observing Satellite, and simulation demonstrate the promising performance and feasibility of the proposed method.

Accounting for model sensitivity in controlled (log)Gaussian geostatistical simulation

Geostatistical simulation using controlled or stratified sampling methods, namely Latin hypercube and stratified likelihood sampling, are capable of generating representative realizations from (log)Gaussian random fields, i.e., spanning efficiently the range of values corresponding to the (log)Gaussian multivariate probability distribution. Although such realizations often serve as parameters for physical process simulators, existing controlled sampling methods do not account for model sensitivity; hence, they need not yield representative realizations of model outputs. To address this shortcoming, controlled sampling methods are embedded within a two-step simulation procedure. The first step involves stratified sampling at a set of control points where attribute values are expected to exert a large impact on model predictions and/or where uncertainty in such predictions is expected to be largest. In the second step, control point samples are used to generate attribute realizations over the entire study region using classical geostatistical simulation. The application of the proposed controlled, two-step, geostatistical simulation procedure is illustrated in a hydrogeological context via a synthetic case study involving physically-based simulation of flow and transport in a porous medium with known boundary and initial conditions over a simple geometrical domain.

Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms

Combination of UAV-based aerial pictures and Structure-from-Motion (SfM) algorithm provides an efficient, low-cost and rapid framework for remote sensing and monitoring of dynamic natural environments. This methodology is particularly suitable for repeated topographic surveys in remote or poorly accessible areas. However, temporal analysis of landform topography requires high accuracy of measurements and reproducibility of the methodology as differencing of digital surface models leads to error propagation. In order to assess the repeatability of the SfM technique, we surveyed a study area characterized by gentle topography with an UAV platform equipped with a standard reflex camera, and varied the focal length of the camera and location of georeferencing targets between flights. Comparison of different SfM-derived topography datasets shows that precision of measurements is in the order of centimetres for identical replications which highlights the excellent performance of the SfM workflow, all parameters being equal. The precision is one order of magnitude higher for 3D topographic reconstructions involving independent sets of ground control points, which results from the fact that the accuracy of the localisation of ground control points strongly propagates into final results.

Imperialist Competitive Algorithm for AUV Path Planning in a Variable Ocean

An imperialist competitive algorithm (ICA) is introduced for solving the optimal path planning problem for autonomous underwater vehicles (AUVs) operating in turbulent, cluttered, and uncertain environments. ICA is a new sociopolitically inspired global search metaheuristic based on a form of competition between “imperialist” forces and opposing colonies. In this study, ICA is applied to optimize the coordinates of a set of control points for generating a curved spline path. The ICA-based path planner is tested to find an optimal trajectory for an AUV navigating through a variable ocean environment in the presence of an irregularly shaped underwater terrain. The genetic algorithm (GA) and quantum-behaved particle swarm optimization (QPSO) are described and evaluated with the ICA for the path optimization problem. Simulation results show that the proposed ICA approach is able to obtain a more optimized trajectory than the GA- or QPSO-based methods. Monte Carlo simulations demonstrate the robustness and superiority of the proposed ICA scheme compared with the GA and QPSO schemes.

New Method for Positional Cartographic Quality Control in Digital Mapping

AbstractQuality control applies to many attributes of a map: completeness, reliability, adherence to symbology specifications, interpretation errors, feature accuracy, and others. This article focuses on a map’s positional accuracy and presents a new method for cartographic quality control applied not just to a single chart, as is usual, but to digital mapping as a whole, generated by a photogrammetric process. The first step is to define the size of the sample using the estimated total number of possible control points in accordance with ISO 2859-1. The second step is to select a set of control points in homogeneous regions defined by a criterion that involves factors such as block adjustment errors, declivity, distance to control points, and operator skills. Then, these control points are surveyed both in a reference and more accurate source and in the cartographic base to be controlled, and the differences between the coordinates of the corresponding points are calculated. Finally, statistical tests ar...

Construction Method of Tailored Facets for Use in Freeform Reflectors Design

ABSTRACTA construction method to obtain sets of tailored facets to be used as the initial configuration in the design of freeform reflector surfaces is presented. The construction strategy starts from a collection of flat elementary facets and achieves the initial configuration of the reflector surface by tailoring the facets according to their Bézier surfaces description. The aim of this research is to describe a procedure capable of generating the Bézier surfaces that define elementary tailored facets; these facets could then be used as initial configurations in the design of freeform reflectors. Though beyond the scope of this article, the facets could be used on a subsequent optimization process; that is, the geometry of the elementary facets could be modified through a global optimization process taking into account all facets, in order to further improve the entire illumination system. Ray tracing is used to calculate the facet’s geometry; it applies elementary merit functions to find the parameters defining the Bézier curve that best meets specifications in each elementary facet. This method is based on a sequential 2D projection strategy that employs different strategic planes for calculations. A method to build up the facet’s geometry using a Bézier surface from 2D curves is provided. This approach will successfully generate a set of control points to describe a Bézier surface compatible with any standard optical optimization tool and suitable for use with computer-aided design (CAD) and other tools that represent solids.

RRAWFLOW: Rainfall-Response Aquifer and Watershed Flow Model (v1.15)

Abstract. The Rainfall-Response Aquifer and Watershed Flow Model (RRAWFLOW) is a lumped-parameter model that simulates streamflow, spring flow, groundwater level, or solute transport for a measurement point in response to a system input of precipitation, recharge, or solute injection. I introduce the first version of RRAWFLOW available for download and public use and describe additional options. The open-source code is written in the R language and is available at http://sd.water.usgs.gov/projects/RRAWFLOW/RRAWFLOW.html along with an example model of streamflow. RRAWFLOW includes a time-series process to estimate recharge from precipitation and simulates the response to recharge by convolution, i.e., the unit-hydrograph approach. Gamma functions are used for estimation of parametric impulse-response functions (IRFs); a combination of two gamma functions results in a double-peaked IRF. A spline fit to a set of control points is introduced as a new method for estimation of nonparametric IRFs. Several options are included to simulate time-variant systems. For many applications, lumped models simulate the system response with equal accuracy to that of distributed models, but moreover, the ease of model construction and calibration of lumped models makes them a good choice for many applications (e.g., estimating missing periods in a hydrologic record). RRAWFLOW provides professional hydrologists and students with an accessible and versatile tool for lumped-parameter modeling.