Calibration Object Research Articles

Making calibration objectives relevant for flood forecasting

A key requirement of an operational flood forecasting model is to provide a forecast lead time that meets the target flood warning lead time and accuracy standards. This represents a challenge, particularly at higher flood warning thresholds due in part to the limitations imposed by a small flood event sample size. This paper looks at developments in model calibration objectives that are relevant to operational flood forecasting and recommends that lead time analysis of the error-corrected model is included in the model calibration process. Parameter updating based on two effective rainfall loss models optimised on saturated and unsaturated catchments is presented as a novel way of improving the performance of an event-based rainfall–runoff model. Developments are illustrated using an operational flood forecasting model for Taunton in south west England. More work is required to assess the sensitivity of model performance to sample size and to assess the relative priority of multiple calibration objectives if there are conflicting results or if accuracy standards are not defined.

Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level

ABSTRACTThis paper proposes a new orientation to address the problem of hydrological model calibration in ungauged basin. Satellite radar altimetric observations of river water level at basin outlet are used to calibrate the model, as a surrogate of streamflow data. To shift the calibration objective, the hydrological model is coupled with a hydraulic model describing the relation between streamflow and water stage. The methodology is illustrated by a case study in the Upper Mississippi Basin using TOPEX/Poseidon (T/P) satellite data. The generalized likelihood uncertainty estimation (GLUE) is employed for model calibration and uncertainty analysis. We found that even without any streamflow information for regulating model behavior, the calibrated hydrological model can make fairly reasonable streamflow estimation. In order to illustrate the degree of additional uncertainty associated with shifting calibration objective and identifying its sources, the posterior distributions of hydrological parameters derived from calibration based on T/P data, streamflow data and T/P data with fixed hydraulic parameters are compared. The results show that the main source is the model parameter uncertainty. And the contribution of remote sensing data uncertainty is minor. Furthermore, the influence of removing high error satellite observations on streamflow estimation is also examined. Under the precondition of sufficient temporal coverage of calibration data, such data screening can eliminate some unrealistic parameter sets from the behavioral group. The study contributes to improve streamflow estimation in ungauged basin and evaluate the value of remote sensing in hydrological modeling. Copyright © 2011 John Wiley & Sons, Ltd.

Analysis of Tsai calibration method using two- and three-dimensional calibration objects

Camera calibration is a fundamental process for both photogrammetric and computer vision. Since the arrival of the direct linear transformation method and its later revisions, new methods have been developed by several authors, such as: Tsai, Heikkila and Zhang. Most of these have been based on the pinhole model, including distortion correction. Some of these methods, such as Tsai method, allow the use of two different techniques for determining calibration parameters: a non-coplanar calibration technique using three-dimensional (3D) calibration objects, and a coplanar technique that uses two-dimensional (2D) calibration objects. The calibration performed by observing a 3D calibration object has good accuracy, and produces very efficient results; however, the calibration object must be accurate enough and requires an elaborate configuration. In contrast, the use of 2D calibration objects yields less accurate results, is much more flexible, and does not require complex calibration objects that are costly to produce. This article compares these two different calibration procedures from the perspective of stereo measurement. Particular attention was focused on the accuracy of the calculated camera parameters, the reconstruction error in the computer image coordinates and in the world coordinate system and advanced image-processing techniques for subpixel detection during the comparison. The purpose of this work is to establish a basis and selection criteria for choosing one of these techniques for camera calibration, according to the accuracy required in each of the many applications using photogrammetric vision: robot calibration methods, trajectory generation algorithms, articulated measuring arm calibration, and photogrammetric systems.

Use of correlation processing of images for determination of distortions and color aberrations of photographic cameras and lenses

The correlation method is used to compare a virtual calibration object and digital images of this object obtained by means of study lenses. The complete arrays of displacements of points of the images relative to the original (characteristics of distortion in the lens) for the entire field of view are found. Distortion arrays and the characteristics of the color aberration for a number of different lenses and digital cameras are determined.

Simulation-based optimal calibration of spark ignition engines with multiple objectives: fuel economy and combustion variability

Optimal engine calibration methodology at part-load operating conditions using a multi-scale simulation approach is proposed and demonstrated on a spark ignition engine with dual-independent variable valve timing and a charge motion control valve. Fuel economy is typically selected as a calibration objective at part-load operating conditions. However, to secure vehicle driveability and smooth engine operation with low vibration and harshness, the combustion variability is considered by introducing the coefficient of variation in the indicated mean effective pressure characterized through a statistical analysis of experimental data. Other engine responses are fully predicted through a co-simulation approach by implementing the quasi-dimensional combustion simulation into the one-dimensional gas exchange simulation, and subsequently captured by an artificial neural network for fast computation. The best actuator set points are determined by solving the constrained multiple-objective optimization problem with fuel economy and combustion variability objectives under partload conditions. The calibration eliminates a high combustion variability at low loads and low speeds, maintaining most of the fuel economy benefit achievable with variable valve timing, and it can be used for an implementable feedforward control strategy.

Quantitative structure–retention relationship of new N‐substituted 2‐alkylidene‐4‐oxothiazolidines

A quantitative structure-retention relationship (QSRR) was built to investigate the relationship between the structural descriptors of 23 newly synthesized N-substituted 2-alkylidene-4-oxothiazolidines and their chromatographic retention. Retention was investigated by means of the reversed-phase thin-layer chromatography (RP-TLC) on the C(18) and CN stationary phases, using methanol, acetonitrile, and tetrahydrofuran as organic modifiers. Full geometry optimization based on the Austin Model 1 (AM1) semi-empirical molecular orbital method was carried out and a set of physicochemical molecular descriptors was calculated from the optimized structures. QSRR was built by means of multiple linear regression (MLR) and partial least squares regression (PLS). The best MLR and PLS models were chosen, on the basis of comparison of the statistical parameters (squared correlation coefficient (R(2)), cross-validated coefficient (R(cv)(2)), and Fischer significance value (F) were used for MLR and the square of the multiple correlation coefficients for the calibration objects (R(2)Y (cum)) and the square of the multiple correlation coefficients for the cross-validation segments (Q(2)Y (cum)) were used for PLS models). Statistically significant and physically meaningful QSRR provided better insight on understanding the retention behavior of the new series of compounds. Lipohilicity (expressed as log P) was included in all MLR and PLS models.

Estimating fisheye camera parameters from homography

This paper presents a method to linearly estimate fisheye camera model parameters from the homography induced by the space plane between two fisheye images. The homography is firstly calculated by using four feature points, instead of three points on the same line, in the fisheye image. And the constraint on the model parameters of fisheye camera can be derived from the homography under the assumption that fisheye camera model is a polynomial model. Then the model parameters for different order polynomials are computed. The proposed technique requires only multiple fisheye images to include a planar scene and need not a priori knowledge of 3D coordinate of the planar scene. Experimental results with synthetic data and real fisheye images demonstrate the validity of our method. The method can also be extended to fisheye image of other planar scene excluding the planar calibration object.

An automated calibration method for non-see-through head mounted displays

Accurate calibration of a head mounted display (HMD) is essential both for research on the visual system and for realistic interaction with virtual objects. Yet, existing calibration methods are time consuming and depend on human judgements, making them error prone, and are often limited to optical see-through HMDs. Building on our existing approach to HMD calibration Gilson et al. (2008), we show here how it is possible to calibrate a non-see-through HMD. A camera is placed inside a HMD displaying an image of a regular grid, which is captured by the camera. The HMD is then removed and the camera, which remains fixed in position, is used to capture images of a tracked calibration object in multiple positions. The centroids of the markers on the calibration object are recovered and their locations re-expressed in relation to the HMD grid. This allows established camera calibration techniques to be used to recover estimates of the HMD display's intrinsic parameters (width, height, focal length) and extrinsic parameters (optic centre and orientation of the principal ray). We calibrated a HMD in this manner and report the magnitude of the errors between real image features and reprojected features. Our calibration method produces low reprojection errors without the need for error-prone human judgements.

Calibrated segmentation of CBCT and CT images for digitization of dental prostheses

State of the art computer aided implant planning procedures typically use a surgical template to transfer the digital 3D planning to the operating room. This surgical template can be generated based on an acrylic copy of the patient's removable prosthesis--the so-called radiographic guide--which is digitized using a CBCT or CT scanner. Since the same accurate fit between the surgical template and the patient as with the radiographic guide and the patient should be ensured, a procedure to accurately digitize this guide is needed. A procedure is created to accurately digitize radiographic guides based on a calibrated segmentation. Therefore, two steps have to be executed. First, during a calibration step a calibration object is CBCT or CT scanned and a calibration algorithm which results in an optimal threshold value is executed. Next the guide is CBCT or CT scanned and a 3D model is created using the obtained optimal threshold. To validate our method, we compared a high accuracy laser scanned copy of the guide with the generated 3D model by creating a distance map between both models. The procedure was performed for different CBCT and CT scanners, and the digitization error for each scanner was defined. The 90th percentile error measured on average 0.15 mm, which was always less than the applied voxel size for all CBCT and CT test scans. The calibration procedure evaluated in this study solves the known problem of digitizing a radiographic guide based on non-standardized gray value CBCT images. The procedure can easily be executed by a clinician and allows an accurate digitization of a radiographic guide using a CBCT or CT scanner. Starting from this digitization, an accurate surgical template can be made which has a good fit on the patient's remaining teeth and surrounding soft tissues.

High-resolution 3D translation measurements using point source x-ray stereoscopy

A method for high-resolution measurement of 3D coordinates and translations of small objects is presented, using single x-ray point-source stereoscopy. The theory of the pinhole method is re-derived for a point-source x-ray projection setup using a conical beam. The method is then implemented using a micro x-ray tomography setup with a single 8 µm point source. Stereo projections are obtained by rotating the object over 90° between subsequent recordings, and microscopic tungsten beads are used as marker points. The accuracy of the method is tested on a spherical calibration object, and found to be better than 10 µm. Using a translation stage, the measurement uncertainty for translation measurements was found to be better than 5 µm along both axes parallel to the detector and at right angles to the detector. Due to the short measurement time and the high resolution, the method will be useful to study the biomechanics of small specimens, and the principle of the method is useful in any cone-beam-based setup.

Calibrating light sources by using a planar mirror

Light source calibration is an important issue in many com- puter vision fields such as photometric stereo and shape from shad- ing. Spheres, with either diffuse or specular reflections, are frequently deployed as calibration objects to recover the direction and intensity of light source from images. We present a novel method for light source calibration by using a planar mirror with a chessboard pattern and a diffuse region. The light direction can be accurately estimated from one mirror orientation by recovering the normal direction of the mirror plane. The location and intensity of light source can be further estimated if two mirror orientations are used. Experimental results show that the calibration accuracy of the proposed method is much higher than traditional sphere-based techniques and can of- fer improved three-dimensional reconstruction in photometric stereo. © 2011 SPIE and IS&T. (DOI: 10.1117/1.3533326)

Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet

Abstract. Rainfall-runoff models are common tools for river discharge estimation in the field of hydrology. In ungauged basins, the dependence on observed river discharge data for calibration restricts applications of rainfall-runoff models. The strong correlation between quantities of river cross-sectional water surface width obtained from remote sensing and corresponding in situ gauged river discharge has been verified by many researchers. In this study, a calibration scheme of rainfall-runoff models based on satellite observations of river width at basin outlet is illustrated. One distinct advantage is that this calibration is independent of river discharge information. The at-a-station hydraulic geometry is implemented to facilitate shifting the calibration objective from river discharge to river width. The generalized likelihood uncertainty estimation (GLUE) is applied to model calibration and uncertainty analysis. The calibration scheme is demonstrated through a case study for simulating river discharge at Pakse in the Mekong Basin. The effectiveness of the calibration scheme and uncertainties associated with utilization of river width observations from space are examined from model input-state-output behaviour, capability of reproducing river discharge and posterior parameter distribution. The results indicate that the satellite observation of the river width is a competent surrogate of observed discharge for the calibration of rainfall-runoff model at Pakse and the proposed method has the potential for improving reliability of river discharge estimation in basins without any discharge gauging.

Contrasting Lumped and Distributed Hydrology Models for Estimating Climate Change Impacts on California Watersheds1

Maurer, Edwin P., Levi D. Brekke, and Tom Pruitt, 2010. Contrasting Lumped and Distributed Hydrology Models for Estimating Climate Change Impacts on California Watersheds. Journal of the American Water Resources Association (JAWRA) 46(5):1024–1035. DOI: 10.1111/j.1752‐1688.2010.00473.xAbstract: We compare the projected changes to streamflows for three Sierra Nevada rivers using statistically downscaled output from 22 global climate projections. The downscaled meteorological data are used to drive two hydrology models: the Sacramento Soil Moisture Accounting model and the variable infiltration capacity model. These two models differ in their spatial resolution, computational time step, and degree and objective of calibration, thus producing significantly different simulations of current and future streamflow. However, the projected percentage changes in monthly streamflows through mid‐21st Century generally did not differ, with the exceptions of streamflow during low flow months, and extreme low flows. These findings suggest that for physically based hydrology models applied to snow‐dominated basins in Mediterranean climate regimes like the Sierra Nevada, California, model formulation, resolution, and calibration are secondary factors for estimating projected changes in extreme flows (seasonal or daily). For low flows, hydrology model selection and calibration can be significant factors in assessing impacts of projected climate change.

Balance between calibration objectives in a conceptual hydrological model

Three different measures to determine the optimum balance between calibration objectives are compared: the combined rank method, parameter identifiability and model validation. Four objectives (water balance, hydrograph shape, high flows, low flows) are included in each measure. The contributions of these objectives to the specific measure are varied to find the optimum balance between the objectives for each measure. The methods are applied to nine middle-sized catchments, using a typical conceptual hydrological model. The results indicate that differences in the optimum balance between the combined rank method and parameter identifiability on the one hand, and model validation on the other, are considerable. The theoretical optimum balance would be a situation without trade-off between single objectives. For some catchments and measures, this situation is closely approximated. On average, the performance of combined rank method is somewhat better than that of parameter identifiability (respectively 3.6% and 5.0% below the theoretical optimum), where the performance of model validation is considerably lower (22.4% below the theoretical optimum). These results are supported by additional validation tests which gave robust results for the combined rank measure and the parameter identifiability measure, and less robust results for the model validation measure. Citation Booij, M. J. & Krol, M. S. (2010) Balance between calibration objectives in a conceptual hydrological model. Hydrol. Sci. J. 55(6), 1017–1032.

Application of a Distributed Large Basin Runoff Model to Lake Erie: Model Calibration and Analysis of Parameter Spatial Variation

The distributed large basin runoff model (DLBRM) was designed to simulate the hydrological processes of the Great Lakes watersheds. As part of its development, the DLBRM was recently applied to 18 watersheds in the Lake Erie basin, where it was first calibrated to reproduce the observed discharge in 1950–1964 and then applied to 1999–2006. Four different calibration objective functions: root mean squared error (RMSE) minimization, mean absolute error (MAE) minimization, correlation maximization, and Nash-Sutcliffe index maximization were tested, revealing RMSE minimization as the most successful method and able to achieve results very close to its global minimum. Further, the distribution of the main DLBRM parameters in the 18 watersheds was consistent with regional patterns, although each watershed was calibrated individually, thus adding credibility to the calibration process. Model performances, while generally good, varied across the basin according to a series of environmental factors, including climate, watershed shape, topography, and land cover and observation factors such as gauging station distribution. Gauging station coverage proved to be extremely important in the ability of the model to track flow variability. The DLBRM proved to be able to replicate well the 1999–2006 hydrologies of most watersheds without recalibration. However, its performance declined in heavily urbanized watersheds, where the landscape changed the most. The results described in this paper will lead to improved model performance and increased practical applications of the DLBRM, providing important information to researchers and decision makers for efficient water management programs in Great Lakes watersheds.

Likelihood parameter estimation for calibrating a soil moisture model using radar bakscatter

Land surface model parameter estimation can be performed using soil moisture information provided by synthetic aperture radar imagery. The presence of speckle necessitates aggregating backscatter measurements over large (> 100 m × 100 m) land areas in order to derive reliable soil moisture information from imagery, and a model calibrated to such aggregated information can only provide estimates of soil moisture at spatial resolutions required for reliable speckle accounting. A method utilizing the likelihood formulation of a probabilistic speckle model as the calibration objective function is proposed which will allow for calibrating land surface models directly to radar backscatter intensity measurements in a way which simultaneously accounts for model parameter- and speckle-induced uncertainty. The method is demonstrated using the NOAH land surface model and Advanced Integral Equation Method (AIEM) backscatter model calibrated to SAR imagery of an area in the Southwestern United States, and validated against in situ soil moisture measurements. At spatial resolutions finer than 100 m × 100 m NOAH and AIEM calibrated using the proposed radar intensity likelihood parameter estimation algorithm predict surface level soil moisture to within 4% volumetric water content 95% of the time, which is an improvement over a 95% prediction confidence of 10% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results suggest that much of this improvement is due to increased ability to simultaneously estimate NOAH parameters and AIEM surface roughness parameters.

Activity Based Matching in Distributed Camera Networks

In this paper, we consider the problem of finding correspondences between distributed cameras that have partially overlapping field of views. When multiple cameras with adaptable orientations and zooms are deployed, as in many wide area surveillance applications, identifying correspondence between different activities becomes a fundamental issue. We propose a correspondence method based upon activity features that, unlike photometric features, have certain geometry independence properties. The proposed method is robust to pose, illumination and geometric effects, unsupervised (does not require any calibration objects). In addition, these features are amenable to low communication bandwidth and distributed network applications. We present quantitative and qualitative results with synthetic and real life examples, and compare the proposed method with scale invariant feature transform (SIFT) based method. We show that our method significantly outperforms the SIFT method when cameras have significantly different orientations. We then describe extensions of our method in a number of directions including topology reconstruction, camera calibration, and distributed anomaly detection.

Neural Network Implementation of Image Rendering via Self-Calibration

This paper proposes a new approach for selfcalibration and color image rendering using Radial Basis Function (RBF) neural network. Most empirical approaches make use of a calibration object. Here, we require no calibration object to both shape recovery and color image rendering. The neural network learning data are obtained through the rotations of a target object. The approach can generate realistic virtual images without any calibration object which has the same reflectance properties as the target object. The proposed approach uses a neural network to obtain both surface orientation and albedo, and applies another neural network to generate virtual images for any viewpoint and any direction of light source. Experiments with real data are demonstrated.

Geometric calibration of a hyperspectral imaging system

Every imaging system requires a geometric calibration to yield accurate optical measurements. Geometric calibration typically involves imaging of a known calibration object and finding the parameters of a camera model and a model of optical aberrations. Optical aberrations can vary significantly across the wide spectral ranges of hyperspectral imaging systems, which can lead to inaccurate geometric calibrations if conventional methods were used. We propose a method based on a B-spline transformation field to align the spectral images of the calibration object to the model image of the calibration object. The degree of spatial alignment between the ideal and the spectral images is measured by normalized cross correlation. Geometric calibration was performed on a hyperspectral imaging system based on an acousto-optic tunable filter designed for the near-infrared spectral range (1.0-1.7microm). The proposed method can accurately characterize wavelength dependent optical aberrations and produce transformations for efficient subpixel geometric calibration.

A Flexible Approach for the Calibration of Biplanar Radiography of the Spine on Conventional Radiological Systems

This paper presents a new method for the calibration of biplanar radiography that makes possible performing 3D reconstructions of the spine using conventional radiological systems. A novel approach is proposed in which a measuring device is used for determining focal distance and have a rough estimation of translation parameters. Using these data, 3D reconstructions of the spine with correct scale were successfully obtained without the need of calibration objects, something that was not previously achieved. For superior results, two optional steps may be executed that involve an optimisation of the geometrical parameters, followed by a scale adjustment with a very simple calibration object. Computer simulations with in vivo CT data show RMS 3D reconstruction errors of 1.9 mm when under the maximum expected variation of the geometrical parameters of the radiological system. Simulations also show that the 3D spinal length may be accurately calculated, but with superior results when using optimisation and adjusting scale (RMS error of 2.09 mm). In vitro experiments with a dried spine composed by 17 vertebrae show mean 3D reconstructions errors of 1.7 mm. We conclude that the proposed method is suitable for use in clinical environment, and that it compares favourably with previous calibration techniques.