Spatial Calibration Research Articles

Euclid II. The VIS instrument

This paper presents the specification, design, and development of the Visible Camera (VIS) on the European Space Agency's mission. VIS is a large optical-band imager with a field of view of 0.54 deg$^2$ sampled at with an array of 609 Megapixels and a spatial resolution of . It will be used to survey approximately 14 000 deg$^2$ of extragalactic sky to measure the distortion of galaxies in the redshift range $z=0.1$--1.5 resulting from weak gravitational lensing, one of the two principal cosmology probes leveraged by With photometric redshifts, the distribution of dark matter can be mapped in three dimensions, and the extent to which this has changed with look-back time can be used to constrain the nature of dark energy and theories of gravity. The entire VIS focal plane will be transmitted to provide the largest images of the Universe from space to date, specified to reach AB with a signal-to-noise ratio S/N in a single broad E (r+i+z)$ band over a six-year survey. The particularly challenging aspects of the instrument are the control and calibration of observational biases, which lead to stringent performance requirements and calibration regimes. With its combination of spatial resolution, calibration knowledge, depth, and area covering most of the extra-Galactic sky, VIS will also provide a legacy data set for many other fields. This paper discusses the rationale behind the conception of VIS and describes the instrument design and development, before reporting the prelaunch performance derived from ground calibrations and brief results from the in-orbit commissioning. VIS should reach fainter than AB =25$ with $ S/N 10$ for galaxies with a full width at half maximum of in a diameter aperture over the Wide Survey, and $m_ AB for a Deep Survey that will cover more than 50 deg$^2$. The paper also describes how the instrument works with the telescope and survey, and with the science data processing, to extract the cosmological information.

Automated inspection approach for OCA particles in multi-layered cover glass of display module assembly

Abstract CG (Cover Glass) is a critical component of electronic screens, and its manufacturing quality is closely relevant to the display effect. To satisfy the requirement of quality control, a machine vision system for real-time inspection of particles in the OCA (Optically Clear Adhesive) layer of CG is presented in this paper. With a brief description of the optical logic of particle imaging and the design of the vision system, our emphasis is put on the post-processing image analysis. To align particles regions in the CG under multi-mode imaging, a spatial alignment calibration algorithm is proposed with perspective distortion correction to calculate the triaxial offset. Then, a CLAHE+PM filtering is adopted to enhance the contrast of the particle. Furthermore, a Meanshift method combined with adaptive local thresholding is proposed to extract the contours of tiny particles. Finally, to distinguish between multiple layers of particles in the CG and detect OCA particles, a combination of fast background reconstruction and Averaged Stochastic Gradient Descent-Support Vector Machine is used. According to in-line experiments and tests, our system can find out a majority of the OCA particles with a PR (over-detection rate) of 1.31% and a PM (miss detection rate) of 0.33% for over 10 000 CG samples.

Spatial calibration of high-density absorption imaging

The accurate determination of atom numbers is an ubiquitous problem in the field of ultracold atoms. For modest atom numbers, absolute calibration techniques are available, however, for large numbers and high densities, the available techniques neglect many-body scattering processes. Here, a spatial calibration technique for time-of-flight absorption images of ultracold atomic clouds is presented. The calibration is obtained from radially averaged absorption images and we provide a practical guide to the calibration process. It is shown that the calibration coefficient scales linearly with optical density and depends on the absorbed photon number for the experimental conditions explored here. This allows for the direct inclusion of a spatially dependent calibration in the image analysis. For typical ultracold atom clouds the spatial calibration technique leads to corrections in the detected atom number up to ≈12% and temperature up to ≈14% in comparison to previous calibration techniques. The technique presented here addresses a major difficulty in absorption imaging of ultracold atomic clouds and prompts further theoretical work to understand the scattering processes in ultracold dense clouds of atoms for accurate atom number calibration.

Accuracy analysis of robotic-assisted immediate implant placement: A retrospective case series

ObjectivesThis study aimed to investigate the accuracy of a robotic computer-assisted implant surgery (r-CAIS) for immediate implant placement. MethodsPatients requiring immediate implant placement in the maxillary anterior region were enrolled for r-CAIS. Before surgery, the patients underwent a cone beam computed tomography (CBCT) scan with a positioning marker. Virtual implant placement position and drilling sequences were planned. Following spatial registration and calibration, the implants were placed with the robotic system under supervision. A postoperative CBCT was taken to control the actual implant positions. The DICOM data of the virtually planned and the actually placed implant were superimposed and registered through the accuracy verification software of the robotic system. The accuracy was calculated automatically. The deviation at the mesial–distal, labial–palatal, and apico-coronal directions were recorded. ResultsFifteen patients with 20 implants were included. No adverse surgical events or postoperative complications were reported. The global platform, apex, and angular deviation were 0.75 ± 0.20 mm (95 % CI: 0.65 to 0.84 mm), 0.70 ± 0.27 mm (95 % CI: 0.57 to 0.82 mm), and 1.17 ± 0.73° (95 % CI: 0.83 to 1.51°), respectively. Moreover, the vertical platform and apex deviation were 0.50 ± 0.31 mm, (95 % CI: 0.35 to 0.64 mm) and 0.48 ± 0.32 mm, (95 % CI: 0.33 to 0.63 mm), respectively. All the placed implant positions were further labial and apical than the planned ones, respectively. ConclusionsHigh accuracy of immediate implant placement was achieved with the robotic system. Clinical significanceOur study provided evidence to support the potential of the robotic system in implant placement, even in challenging scenarios.

Abstract 3033: Registration And Segmentation Framework For 3d Instrument Visualization Relative To Preoperative Ct Imaging Via Spatially-tracked Ultrasound: A Phantom Study

Objectives: Surgical navigation guides surgery without radiation by mapping instrument locations onto preoperative CT (pCT). Adoption to vascular surgery remains novel. Feasibility of spatially-tracked ultrasound (US) for surgical registration and wire localization relative to pCT is demonstrated. Methods: pCT was taken (0.6mm) of a carotid artery phantom and a CT artery model (CTM) segmented. An US probe (Philips L17-5), was rigidly attached to an optical tracker and swept along the artery (1000 images). A navigation system (Medtronic StealthStation) acquired the tracker and 2D US image's positions via a spatial calibration (1.6mm accuracy). The artery in each image was segmented and tracking data combined to reconstruct a 3D US artery model (USM) (Fig. 1). The CTM and USM were registered via an iterative closest point algorithm. A Rosen wire (0.035in) was inserted into the artery. Acquiring an US sweep of the wire (500 images), it was segmented in each image via thresholding. Segmentations and tracking were combined into a 3D US wire-model (USWM). Its position was overlaid onto pCT via the USM-CTM registration. Another CT was acquired, registered via a fiducial point-based registration (0.86 mm accuracy), and the ground truth wire position segmented (Fig. 2). A tip-to-tip distance was measured between the USWM and CT wire positions. Results: CTM-to-USM registration error was 0.93 mm (RMSE) between artery point clouds. The tip-to-tip distance between CT and USWM wire models was 0.2 (longitudinal) and 1.4 (radial) mm. Conclusions: A vascular navigation framework capable of patient registration and highly accurate instrument localization is provided using tracked iUS.

Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

Bearing fault diagnosis based on data missing and feature shift suppression strategy

To mitigate the impact of fault iconic feature shift and feature missing due to missing data values on bearing fault diagnosis, this paper proposes a fault diagnosis method based on a spatial frequency filter and a Multi-Scale feature recombination calibration network (MSRCN). First, the fault features are converted into frequency band features and feature enhancement is realized using Mel filters to weaken the effect of fault feature offset. Then, the spatial calibration module (SC) in the MSRCN is utilized to further improve the fault feature distribution and eliminate the fault feature offset problem. Next, to solve the fault feature missing problem, the remaining fault features are sampled by multi-scale reorganization using MSRCN to obtain new fault features, which overcomes the effect of fault feature missing on fault diagnosis. Finally, experiments are conducted on CWRU and XJTU-SY rolling bearing datasets to verify that the algorithm can effectively solve the fault feature offset and missing problem. Meanwhile, the experimental results prove that the algorithm proposed in this paper can realize high-precision fault diagnosis.

Learning Joint Local-Global Iris Representations via Spatial Calibration for Generalized Presentation Attack Detection

Learning Joint Local-Global Iris Representations via Spatial Calibration for Generalized Presentation Attack Detection

On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow

Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.

Improving MODIS-IR precipitable water vapor based on the FIDWFT model

The monitoring of precipitable water vapor (PWV) is of significant importance for meteorological research and rainstorm prediction. The moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used remote sensing PWV instruments aboard the Terra and Aqua satellites. The main objective of this study is to develop a fused inverse distance weighting and the Fourier transform model (FIDWFT) to calibrate MODIS Infrared (IR) PWV. In this research, one year of ground observation data from 17 GPS stations were utilized to evaluate the accuracy of MODIS IR PWV in Hong Kong. Initially, the PWV obtained by one radiosonde was used to verify the GPS PWV. Comparison between GPS and radiosonde showed good agreement with an R-squared of 0.97. After the estimation of GPS PWV in the study area was evaluated, MODIS IR products in the study area were extracted for evaluation. The comparison between MODIS and GPS showed that the R-squared is 0.81 and the mean bias (MB) is −1.60 mm. Considering the spatial interpolation and calibration problems of MODIS products, new model used an inverse distance weighting method to make MODIS IR PWV and GPS PWV completely coincide in space, and fifteen MODIS pixel points were selected for interpolation. Then the MODIS IR PWV was calibrated by GPS PWV using Fourier transform model. Results showed that compared with the MODIS IR PWV before calibration, the R-squared increases from 0.81 to 0.94, the root mean square error decreases from 8.72 mm to 4.98 mm, and the MB decreases from −1.60 mm to −0.83 mm. Therefore, the method is feasible to estimate MODIS IR water vapor, and it achieves better results.

Two-Step LiDAR/Camera/IMU Spatial and Temporal Calibration Based on Continuous-Time Trajectory Estimation

Two-Step LiDAR/Camera/IMU Spatial and Temporal Calibration Based on Continuous-Time Trajectory Estimation

Spatial quadric calibration method for multi-line laser based on diffractive optical element

The traditional multi-line laser calibration method relies primarily on calibrating the spatial plane equations of multiple laser lines and reconstructing the multi-line laser in three dimensions through the plane equation and camera parameters. In traditional methods, the multi-line laser is projected as a straight line by default, but in reality, the laser line projected by the multi-line laser is not a plane due to the Diffractive Optical Element (DOE). Instead, it is a surface with a certain curvature in space. During the production process, the multi-line laser experiences significant distortion because of the DOE projecting at a large angle. Consequently, if traditional methods are used, accurate calibration of the multi-line laser cannot be achieved. To address this issue, this paper introduces a new calibration algorithm for multi-line laser based on a spatial quadric equation. By calibrating the spatial quadric equation of the multi-line laser, the spatial characteristics of the laser can be accurately captured. The algorithm involves projecting a crossed 7-line laser onto a calibration plate and changing the position of the calibration plate while capturing images. This allows for obtaining the spatial three-dimensional coordinates of each laser line in the camera coordinate system. Next, spatial quadric equations are fitted for each laser line based on the three-dimensional coordinates. Finally, 3D reconstruction is performed using the quadratic surface parameters of the multi-line laser and camera parameters. The results from 3D reconstruction demonstrate that, compared to traditional laser calibration methods based on planes, the proposed algorithm achieves higher calibration accuracy. It also improves the number of successful binocular multi-line laser matches and overall accuracy.

Unravelling the impact of spatial discretization and calibration strategies on event-based flood models

Unravelling the impact of spatial discretization and calibration strategies on event-based flood models

Changes in China's Snow Droughts Characteristics From 1993 to 2019

AbstractSnowpacks are natural water reservoirs providing a considerable amount of water for humans and ecosystems. However, current global snow products (e.g., ESA GlobSnow v3.0), lack high spatial resolution and regional calibrations necessary to capture the high heterogeneity of snow water equivalents (SWEs) in complex Asian mountainous terrains. Therefore, our understanding of snow drought characteristics in China remains limited. Herein, we used an improved SWE product calibrated specifically for China to explore the characteristics of snow droughts, delineated by a standardized SWE index (SWEI) between 1993 and 2019. Our analysis was focused over three main snow‐covered regions of China: Qinghai–Tibet Plateau (QTP), northern Xinjiang, Northeast China. Especially during the period from 1993 to 2010, we found that the SWEI increased significantly at rates of 0.022/yr (Northeast China), 0.017/yr (northern Xinjiang), and 0.011/yr (QTP) (p < 0.01, Mann‐Kendall trend test). Increased SWEI contributed to decreasing snow drought events across China, with an obvious short‐term characteristic, whilst area proportion of the identified 1‐month snow droughts was above 46.5% across three regions. Furthermore, we found that the occurrence of snow droughts was likely mediated by large‐scale atmospheric circulation, since increased water vapor transport caused a significant vapor flux convergence in cold seasons over three regions, especially in northern Xinjiang and Northeast China.

Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins

AbstractOptimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

STCFCM: A Spatial and Temporal Cloud Fraction-Based Calibration Method for Satellite-Derived Near-Infrared Water Vapor Product

STCFCM: A Spatial and Temporal Cloud Fraction-Based Calibration Method for Satellite-Derived Near-Infrared Water Vapor Product

Can local drain flow measurements be utilized to improve catchment scale modelling?

Tile drains constitute a shortcut from agricultural fields to surface water systems, significantly altering the transport pathways and fate of nitrate during transport. A correct representation of tile drainage flow is thus crucial for estimating nitrate load at the catchment scale and to identify optimal locations for N-mitigation measures. Drainage is a local process, controlled by local properties and drain configurations, which are rarely known for individual fields, making drainage flow and transport a challenging task in catchment scale models. This study tests the potential for improving drainage flow dynamics at catchment scale, by utilising local drainage flow measurements in a spatial calibration scheme. A distributed hydrological model, MIKE SHE, for the agricultural-dominated Norsminde catchment (145 km2) in Denmark, was calibrated using spatially distributed surrogate parameters (pilot points) to represent heterogeneity in the soil (top 3 m) and the deeper geology below 3 m. The model was calibrated using hydraulic heads, stream discharge, and measured drainage flow from eight drain catchments. Drain measurements were very important in guiding the calibration of top 3 m and subsurface pilot points located in the drainage fields, showing that drain flow hold information on both local (shallow) and regional (deeper) flow patterns. Contrarily, pilot points located outside the drained fields were mainly sensitive to the hydraulic head measurements and the summer water balance of the stream discharge on a catchment scale. Consequently, incorporation of the drain data improved local performance, but did not improve the parameterization and drain description of the entire catchment. Exploitation of the drain flow information is thus difficult beyond the drain catchments, and other approaches are needed to extrapolate and exploit the local data.

An integrated hinged dual-probe for co-target fast switching imaging.

The diversity of functional applications of atomic force microscopes is the key to the development of nanotechnology. However, the single probe configuration of the traditional atomic force microscope restricts the realization of different application requirements for the same target area of a single sample, and the replacement of the working probe will lead to the loss of the target area. Here, the design, simulation, fabrication, and application of a unique atomic force microscope dual-probe are presented, which consists of a pair of parallel cantilevers with a narrow gap and a U-shaped hinged probe base. The Integrated Hinged Dual-Probe (IHDP) is developed specifically for fast switching of probes working in limited space and independent and precise manipulation of each probe. The deflection signal sensing of two cantilevers is achieved simultaneously by a single laser beam, and the decoupled independent cantilever deflection signals do not interfere with each other. The switching of the working probe is achieved by a piezoelectric ceramic with a 2 µm stroke and U-shaped hinge structure, which is fast and does not require tedious and repetitive spatial position calibration. By measuring standard grid samples, IHDP exhibits excellent measurement and characterization capabilities. Finally, a working probe switching imaging experiment was conducted on solidified rat cardiomyocytes, and the experimental process and imaging results demonstrated the superiority of IHDP in switching probe scanning imaging of the same target area of a single sample. The two probes of IHDP can undergo arbitrary functionalization modifications, which helps achieve multidimensional information acquisition for a single target.

A Novel Lightweight Object Detection Network with Attention Modules and Hierarchical Feature Pyramid

Object detection methods based on deep learning typically require devices with ample computing capabilities, which limits their deployment in restricted environments such as those with embedded devices. To address this challenge, we propose Mini-YOLOv4, a lightweight real-time object detection network that achieves an excellent trade-off between speed and accuracy. Based on CSPDarknet-Tiny as the backbone network, we enhance the detection performance of the network in three ways. We use a multibranch structure embedded in an attention module for simultaneous spatial and channel attention calibration. We design a group self-attention block with a symmetric structure consisting of a pair of complementary self-attention modules to mine contextual information, thereby ensuring that the detection accuracy is improved without increasing the computational cost. Finally, we introduce a hierarchical feature pyramid network to fully exploit multiscale feature maps and promote the extraction of fine-grained features. The experimental results demonstrate that Mini-YOLOv4 requires only 4.7 M parameters and has a billion floating point operations (BFLOPs) value of 3.1. Compared with YOLOv4-Tiny, our approach achieves a 3.2% improvement in mean accuracy precision (mAP) for the PASCAL VOC dataset and obtains a significant improvement of 3.5% in overall detection accuracy for the MS COCO dataset. In testing with an embedded platform, Mini-YOLOv4 achieves a real-time detection speed of 25.6 FPS on the NVIDIA Jetson Nano, thus meeting the demand for real-time detection in computationally limited devices.

A Miniature Dual-Fiber Probe for Quantitative Optical Coherence Elastography.

We present a miniaturized dual-fiber OCE probe of 1 mm diameter allowing for robust shear wave elastography. Shear wave velocity is estimated between two optics and hence independent of wave propagation between excitation and imaging. We quantify the wave propagation by evaluating either a single or two measurement points. Particularly, we compare both approaches to ultrasound elastography. Our experimental results demonstrate that quantification of local tissue elasticities is feasible. For homogeneous soft tissue phantoms, we obtain mean deviations of 0.15 ms-1 and 0.02 ms-1 for single-fiber and dual-fiber OCE, respectively. In inhomogeneous phantoms, we measure mean deviations of up to 0.54 ms-1 and 0.03 ms-1 for single-fiber and dual-fiber OCE, respectively. We present a dual-fiber OCE approach that is much more robust in inhomogeneous tissues. Moreover, we demonstrate the feasibility of elasticity quantification in ex-vivo coronary arteries. This study introduces an approach for robust elasticity quantification from within the tissue.