Calibration Scene Research Articles

On-Orbit Verification of RL-Based APC Calibrations for Micrometre Level Microwave Ranging System

Micrometre level ranging accuracy between satellites on-orbit relies on the high-precision calibration of the antenna phase center (APC), which is accomplished through properly designed calibration maneuvers batch estimation algorithms currently. However, the unmodeled perturbations of the space dynamic and sensor-induced uncertainty complicated the situation in reality; ranging accuracy especially deteriorated outside the antenna main-lobe when maneuvers performed. This paper proposes an on-orbit APC calibration method that uses a reinforcement learning (RL) process, aiming to provide the high accuracy ranging datum for onboard instruments with micrometre level. The RL process used here is an improved Temporal Difference advantage actor critic algorithm (TDAAC), which mainly focuses on two neural networks (NN) for critic and actor function. The output of the TDAAC algorithm will autonomously balance the APC calibration maneuvers amplitude and APC-observed sensitivity with an object of maximal APC estimation accuracy. The RL-based APC calibration method proposed here is fully tested in software and on-ground experiments, with an APC calibration accuracy of less than 2 mrad, and the on-orbit maneuver data from 11–12 April 2022, which achieved 1–1.5 mrad calibration accuracy after RL training. The proposed RL-based APC algorithm may extend to prove mass calibration scenes with actions feedback to attitude determination and control system (ADCS), showing flexibility of spacecraft payload applications in the future.

30‐3: A Moving Camera and Synthetic Calibration Target Solution for Non‐Planar Scene Estimation and Projector Calibration

The automatic calibration of projector systems improves the possible environments available to this technology which relies on exact scene calibration. This paper explores the challenge of projector calibration and non‐planar scene estimation. This formulation assumes no prior information on the moving camera or fixed projector. Limited scene understanding provides the basis for constructing synthetic calibration targets to perform a geometric recovery through bundle adjustment. Synthetic data are generated from ground‐truth camera and projector parameters to explore the method's performance over varying properties of the synthetic calibration targets.

A Hybrid Calibration Method for the Binocular Omnidirectional Vision System

Omnidirectional three-dimensional (3D) measurement based on the binocular omnidirectional vision system attracts increasing attention in computer vision. A 360° horizontal field of view (FOV) and 180° vertical FOV are obtained by the binocular omnidirectional vision system with only a pair of images. However, a nonlinear relationship exists between points on image planes and in 3D space for the binocular omnidirectional vision system, and traditional calibration methods are no longer applicable. This study proposes a hybrid calibration approach, which effectively fuses the unified camera model and the backpropagation neural network with a virtual 3D target, in which the neural network improved with genetic algorithm compensates all kinds of distortions and errors. First, the unified camera model is used to calibrate the intrinsic parameters, extrinsic parameters, and a transformation matrix. Second, the pixel coordinates on image planes are converted to 3D coordinates by 3D reconstruction through binocular vision. The errors between actual and calculated 3D coordinates are obtained. In addition, the neural network improved with genetic algorithm is established and trained with pixel coordinates on image planes and errors. Experimental results indicate that the average measurement errors in the X and Y directions of the central area are respectively reduced to 0.1057 and 0.1548 mm, and those of the edge area are reduced to 0.1992 and 0.2748 mm. Compared with mathematical and neural network methods, the hybrid calibration method provides more stable, robust, and accurate results. It is proven suitable for complex calibration scenes with large FOV, high distortion, and strong nonlinearity.

A Nonuniformity Correction Method Based on Gradient Scene Calibration

The nonuniformity of infrared focal plane array (IRFPA) seriously affects the image quality and the measurement accuracy of the infrared imaging system. The two-point nonuniformity correction (NUC) method based on blackbody calibration performs a less than very satisfactory result to correct the sky scene with gradient gray distribution. Aiming at this problem, a new NUC method based on gradient scene calibration is proposed. First, the correction models are deduced in theory with the reference of the blackbody and the gradient scene. Then the pure sky images at the two different pitch angles are used as two reference temperature points to obtain the gain and offset coefficients by two-point correction. Next, two correction coefficients are filtered to remove residual noise. Finally, an outfield experiment with the proof-of-concept camera is performed. Experimental results show that the signal-to-noise ratio (SNR) of the point target image corrected by the proposed method is increased from 5.56 to 9.84 compared with the blackbody calibration-based NUC. And it has the preference of low complexity and easy implementation in the real-time system.

A Fast Calibration Method for Photonic Mixer Device Solid-State Array Lidars.

The photonic mixer device (PMD) solid-state array lidar, as a three-dimensional imaging technology, has attracted research attention in recent years because of its low cost, high frame rate, and high reliability. To address the disadvantages of traditional PMD solid-state array lidar calibration methods, including low calibration efficiency and accuracy, and serious human error factors, this paper first proposes a calibration method for an array complementary metal–oxide–semiconductor photodetector using a black-box calibration device and an electrical analog delay method; it then proposes a modular lens distortion correction method based on checkerboard calibration and pixel point adaptive interpolation optimization. Specifically, the ranging error source is analyzed based on the PMD solid-state array lidar imaging mechanism; the black-box calibration device is specifically designed for the calibration requirements of anti-ambient light and an echo reflection route; a dynamic distance simulation system integrating the laser emission unit, laser receiving unit, and delay control unit is designed to calibrate the photodetector echo demodulation; the checkerboard calibration method is used to correct external lens distortion in grayscale mode; and the pixel adaptive interpolation strategy is used to reduce distortion of distance images. Through analysis of the calibration process and results, the proposed method effectively reduces the calibration scene requirements and human factors, meets the needs of different users of the lens, and improves both calibration efficiency and measurement accuracy.

카메라 캘리브레이션을 위한 영상의 주파수 특성 기반 스테레오 카메라의 상대적인 회전값 추정

카메라 캘리브레이션을 위한 영상의 주파수 특성 기반 스테레오 카메라의 상대적인 회전값 추정

Fast System Calibration With Coded Calibration Scenes for Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the field-of-view (FOV). The scanned sample has the maximum size of a voxel so that the calibration measurements have relatively poor signal-to-noise ratio (SNR). In this paper, we present the coded calibration scene (CCS) framework, where we place multiple MNP samples inside the FOV in a random or pseudo-random fashion. Taking advantage of the sparsity of the SM, we reconstruct the SM by solving a convex optimization problem with alternating direction method of multipliers using CCS measurements. We analyze the effects of filling rate, number of measurements, and SNR on the SM reconstruction using simulations and demonstrate different implementations of CCS for practical realization. We also compare the imaging performance of the proposed framework with that of a standard compressed sensing SM reconstruction that utilizes a subset of calibration measurements from a single MNP sample. The results show that CCS significantly reduces calibration time while increasing both the SM reconstruction and image reconstruction performances.

MOBILE LASER SCANNING SYSTEMS FOR GPS/GNSS-DENIED ENVIRONMENT MAPPING

Abstract. Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy.

Retardation Plate for the Calibration of a Fully Polarimetric Radiometer: Determination of Characteristics

This paper presents a novel method to determine the phase shift of a phase retardation plate. Primarily, the method has been developed for fully polarimetric calibration standards, which consist of a linearly polarized (tripolarimetric) calibration reference and a retardation plate. However, the method can also be applied to phase retardation plates for other purposes. The method presented here applies a fully polarimetric radiometer as the measuring instrument and it is based on the application of both tripolarimetric and fully polarimetric calibration data, generated by the calibration standard. In the same process, the skew angle between the coordinates of the retardation plate and antenna polarization basis is determined. In addition, a method to determine the insertion loss of the phase retardation plate is described. It is based on the difference in the brightness temperatures when the cold calibration scene is measured with and without the retardation plate in the RF path. The feasibility of the methods presented here has been demonstrated using Aalto University's Fully Polarimetric Radiometer (FPoR), the Fully Polarimetric Calibration Standard (FPCS), and its retardation plate. A good match between the measurement results and theoretical predictions, as well as good repeatability, was achieved in determining the parameters of the retardation plate in 11 distinct measurements over a time period of almost three years. The results suggest that the method is sufficiently accurate in terms of the calibration accuracy requirements for airborne and spaceborne remote sensing radiometers.

Application of Image Processing and Three-Dimensional Data Reconstruction Algorithm Based on Traffic Video in Vehicle Component Detection

Vehicle detection is one of the important technologies in intelligent video surveillance systems. Owing to the perspective projection imaging principle of cameras, traditional two-dimensional (2D) images usually distort the size and shape of vehicles. In order to solve these problems, the traffic scene calibration and inverse projection construction methods are used to project the three-dimensional (3D) information onto the 2D images. In addition, a vehicle target can be characterized by several components, and thus vehicle detection can be fulfilled based on the combination of these components. The key characteristics of vehicle targets are distinct during a single day; for example, the headlight brightness is more significant at night, while the vehicle taillight and license plate color are much more prominent in the daytime. In this paper, by using the background subtraction method and Gaussian mixture model, we can realize the accurate detection of target lights at night. In the daytime, however, the detection of the license plate and taillight of a vehicle can be fulfilled by exploiting the background subtraction method and the Markov random field, based on the spatial geometry relation between the corresponding components. Further, by utilizing Kalman filters to follow the vehicle tracks, detection accuracy can be further improved. Finally, experiment results demonstrate the effectiveness of the proposed methods.

Four-dimensional (4D) tracking of high-temperature microparticles

High-speed tracking of hot and molten microparticles in motion provides rich information about burning plasmas in magnetic fusion. An exploding-wire apparatus is used to produce moving high-temperature metallic microparticles and to develop four-dimensional (4D) or time-resolved 3D particle tracking techniques. The pinhole camera model and algorithms developed for computer vision are used for scene calibration and 4D reconstructions. 3D positions and velocities are then derived for different microparticles. Velocity resolution approaches 0.1 m/s by using the local constant velocity approximation.

Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras.

Since it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, an object detected in a camera has a different shape in another camera, which is a critical issue of wide-range, real-time surveillance systems. In order to address the problem, this paper presents an object retrieval method by extracting the normalized metadata of an object-of-interest from multiple, heterogeneous cameras. The proposed metadata generation algorithm consists of three steps: (i) generation of a three-dimensional (3D) human model; (ii) human object-based automatic scene calibration; and (iii) metadata generation. More specifically, an appropriately-generated 3D human model provides the foot-to-head direction information that is used as the input of the automatic calibration of each camera. The normalized object information is used to retrieve an object-of-interest in a wide-range, multiple-camera surveillance system in the form of metadata. Experimental results show that the 3D human model matches the ground truth, and automatic calibration-based normalization of metadata enables a successful retrieval and tracking of a human object in the multiple-camera video surveillance system.

Calibration and object correspondence in camera networks with widely separated overlapping views

This study contributes in two ways to the research of multi-camera object tracking in the context of visual surveillance. Firstly, a semi-automatic scene calibration method is proposed to deal with mapping a network of cameras with overlapped fields of view onto a single ground plane view, even when the overlap is not substantial. The proposed method uses a semi-supervised approach that combines tracked blobs with user-selected line scene features to recover the homographies between camera views that are both simple and accurate. Then, based on the scene calibration information, the intersection points of the projected vertical axis of single camera blobs are used to make object correspondences across multiple views. The method works in mixed environments of both pedestrians and vehicles, and is shown to be accurate and robust against segmentation noise and occlusions. Finally, the advantage of the proposed method is demonstrated by quantitative tracking performance evaluation and comparison against previous methods.

Automatic scene calibration for detecting and tracking people using a single camera

Segmenting and tracking of people is an important aim in the video analysis with multitudinous applications. The scene calibration enables the system to process the input video in a different way depending on the camera position and the scene characteristics for arising the successful results. In complex situations there exists extended occlusions, shadows and/or rejections, so that an appropriate calibration is required in order to achieve a highly developed people's segmentation as well as a tracking algorithm. In the majority of cases, once the system has been installed in a certain scene, it is difficult to obtain the calibration information of the scene. In this paper, an automatic method to calibrate the scene for detecting and tracking people systems is presented based on measurements of video sequences captured from a stationary camera.

On the calibration of high dynamic range photography for luminance measurements in indoor daylit scenes

This article describes a study of two issues concerning the calibration of high dynamic range (HDR) photography for luminance measurements in indoor daylit spaces: (i) the selection of a target in a scene for calibration and (ii) the variation of the calibration factor with different daylight levels. Luminance measurements of coloured and grey targets were conducted in a classroom under stable sky conditions using a calibrated luminance meter and HDR photography. Comparisons between luminance values measured by HDR photography and the spot luminance meter were carried out. This study affirms the findings of a previous study that grey targets are more suitable for use in the calibration of HDR photography for luminance measurements. Using a grey surface of 18% reflectance, the ratio between luminances obtained by the two methods, regarded as the calibration factor, was computed. It was found that there was a weak and insignificant correlation between calibration factor and daylight levels within the range of investigation in this study.

Contextual Information and Covariance Descriptors for People Surveillance: An Application for Safety of Construction Workers

In computer science, contextual information can be used both to reduce computations and to increase accuracy. This paper discusses how it can be exploited for people surveillance in very cluttered environments in terms of perspective (i.e., weak scene calibration) and appearance of the objects of interest (i.e., relevance feedback on the training of a classifier). These techniques are applied to a pedestrian detector that uses a LogitBoost classifier, appropriately modified to work with covariance descriptors which lie on Riemannian manifolds. On each detected pedestrian, a similar classifier is employed to obtain a precise localization of the head. Two novelties on the algorithms are proposed in this case: polar image transformations to better exploit the circular feature of the head appearance and multispectral image derivatives that catch not only luminance but also chrominance variations. The complete approach has been tested on the surveillance of a construction site to detect workers that do not wear the hard hat: in such scenarios, the complexity and dynamics are very high, making pedestrian detection a real challenge.

Calibration algorithm for Fourier transform spectrometer with thermal instabilities

Complex domain calibration is an efficient method to correct the amplitude and phase of a spectrum obtained from a Fourier transform spectrometer. This method is, however, not directly applicable in the occurrence of a zero path difference (ZPD) shift between a scene interferogram and calibration blackbody interferograms. This situation is likely to happen for a system with thermal instabilities. It is found that a ZPD shift smaller than 1 sampling point can cause a large disagreement between the spectra evaluated from the two interferometer sweep directions. We have developed an algorithm for a complex calibration in the presence of ZPD shifts. The restricting aspect of the real-time capability is taken into account.

Object-Oriented Color Calibration of Multi-viewpoint Cameras in Sparse and Convergent Arrangement

This paper proposes a new color calibration method for multi-viewpoint images captured by sparsely and convergently arranged cameras. The main contribution of our method is its practical and efficient procedure while traditional methods are known to be labor-intensive. Because our method automatically extracts 3D points in the scene for color calibration, we do not need to capture color calibration objects like Macbeth chart. This enables us to calibrate a set of multi-viewpoint images whose capture environment is not available. Experiments with real images show that our method can minimize the difference of pixel values (1) quantitatively by leave-one-out evaluation, and (2) qualitatively by rendering a 3D video.

Development and Calibration of SMOS Reference Radiometer

Three flight models (FMs) of the reference radiometer of the Soil Moisture and Ocean Salinity (SMOS) mission have been developed and tested. SMOS is a joint mission of the European Space Agency, Centre National d'Etudes Spatiales of France, and Centre for the Development of Industrial Technology of Spain. The reference radiometer is a noise injection radiometer (NIR); the NIR subsystem FM has been developed by Elektrobit Microwave, Ltd., in collaboration with the Laboratory of Space Technology of Helsinki University of Technology, which has acted as a subcontractor. The NIR subsystem will be integrated into the microwave imaging radiometer using aperture synthesis (MIRAS) payload in 2006. MIRAS will be the sole instrument onboard the SMOS satellite. MIRAS has 66 total power receiver units (light and cost-effective front end) and three NIR units. The purpose of the NIR subsystem is 1) to provide precise measurement of the average brightness temperature scene for absolute calibration of the MIRAS image map, 2) to measure the noise temperature level of the internal active calibration sources of MIRAS [referred to as the calibration subsystem (CAS)], and 3) to form interferometer baselines, so-called mixed baselines, with the regular receiver units. The performance of the NIR is a decisive factor of overall MIRAS performance. In this paper, we present the design solutions for the NIR FMs, which enable the achievement of the mission goals set for the NIR subsystem. The results of the NIR test campaign, proving that the performance and environmental requirements are fulfilled, are also presented, and the outcome of the ground calibration campaign is analyzed. Furthermore, the orbital calibration scheme is depicted; the calibration scheme enables the NIR to measure its targets with precision.

A system for the capture and synthesis of insect motion

We present an integrated system that enables the capture and synthesis of 3D motions of small scale dynamic creatures, typically insects and arachnids, in order to drive computer generated models. The system consists of a number of stages, initially, the acquisition of a multi-view calibration scene and synchronised video footage of a subject performing some action is carried out. A user guided labelling process, that can be semi-automated using tracking techniques and a 3D point generating algorithm, then enables a full metric calibration and captures the motions of specific points on the subject. The 3D motions extracted, which often come from a limited number of frames of the original footage, are then extended to generate potentially infinitely long, characteristic motion sequences for multiple similar subjects. Finally a novel path following algorithm is used to find optimal path along with coherent motion for synthetic subjects.