Interior Camera Parameters Research Articles

A calibration and prediction method of the camera imaging parameters in variable temperature environment

In the on-orbit application of photogrammetry, constraints on thermal control resources or aging of thermal control equipment will trigger increased fluctuations in the camera operating environment temperature. Consequently, the parameters of the camera imaging model vary, such that the measurement accuracy is reduced. Accordingly, the accurate prediction of the camera imaging parameters (e.g., Camera Interior Parameters (CIP) and Lens Distortion Parameters (LDP)) at variable temperatures takes on critical significance.First, a novel method, termed as Virtual Multi-station Camera Calibration (VMCC), is proposed to calibrate the CIP and LDP with each frame at variable temperatures. Second, the Variational Mode Decomposition method combined with the Northern Goshawk Optimizer (NGO-VMD) is employed to decompose the camera imaging parameter signals obtained from VMCC, yielding the n Intrinsic Mode Functions (IMFs) contained in the respective parameter signal. Third, the Generalized Regression Neural Network combined with the Dung Beetle Optimizer (DBO-GRNN) is employed, such that the smoothness parameter q of the GRNN is iteratively optimized based on the camera imaging parameter prediction error. Lastly, the CIP and the LDP are accurately predicted at different temperatures in accordance with the temperature data from multiple parts of the camera.Based on this study, a reliable dataset of camera imaging parameter responses at variable temperatures can be generated for the on-orbit photogrammetry system. Furthermore, the prediction accuracy and immense application potential of neural network modeling methods are demonstrated in camera parameter prediction.

Camera optimal projection model identification and calibration method based on the NGO-BA architecture.

In order to bridge the fundamental commonalities between imaging models of camera lenses with different principles and structures, allowing for an accurate description of imaging characteristics across a wide range of field-of-view (FOV), we have proposed a generic camera calibration method on the basis of the projection model optimization strategy. First, in order to cover the current mainstream projection models, piecewise functions for geometric projection models and a polynomial function for the fitting projection model are designed. Then, the corresponding camera multistation self-calibration bundle adjustment (BA) module is developed for various projection models. Further, by integrating the self-calibration BA algorithm into the northern goshawk optimization architecture, iterative optimization is performed on the projection model adjustment parameters, camera interior parameters, camera exterior parameters, and lens distortion parameters until the target reprojection (RP) error reaches the global minimum. The experimental results indicate that the calibration RP root mean square error in this method is 1/20 pixel for a 68° FOV camera, 1/13 pixel for an 84° FOV camera, 1/9 pixel for a 115° FOV camera, 1/9 pixel for a 135° FOV camera, and 1/6 pixel for a 180° FOV camera. This calibration method offers fast and versatile optimization for various projection model types, encompassing a wide range of projection functions. It can efficiently determine the optimal projection model and all imaging parameters for multiple cameras during the calibration process.

Principles of self-calibration and visual effects for digital camera distortion

Abstract Producing accurate spatial data with stereo photogrammetric techniques is a challenging task, and the central projection of the space needs to be defined as closely as possible to its real form in each image taken for the relevant production. Interior camera parameters that define the exact imaging geometry of the camera and the exterior orientation parameters that locate and rotate the imaging directions in a coordinate system have to be known accurately for this correct definition. All distortions sourcing from lens and sensor planes and their recording geometry are significant as they are not suitable for detection with manual measurements. It is of vital importance to clearly understand the camera self-calibration concept with respect to the lens and the sensor plane geometry and include every possible distortion source as an unknown parameter in the calibration adjustments as they are all modellable systematic errors. In this study, possible distortion sources and self-calibration adjustments are explained in detail with a recently developed visualization software. The distortion sources investigated in the study are radial, tangential, differential scale, and axial skewing distortion. Thanks to the developed software, image center point, distorted grids, undistorted grids, and principal points were visualized. As a result, the most important element of obtaining accurate and precise photogrammetric productions is the correct definition of the central projection of the space for each image, and therefore, the study explains an accurate and robust procedure with the correct definition and use of correct camera internal parameters.

All-parameter calibration method of the on-orbit multi-view dynamic photogrammetry system.

Photogrammetry (PG) can present accurate data to evaluate the functional performance of large space structures. For camera calibration and orientation, the On-orbit Multi-view Dynamic Photogrammetry System (OMDPS) lacks appropriate spatial reference data. A multi-data fusion calibration method for all parameters for this kind of system is proposed in this paper as a solution to this issue. Firstly, a multi-camera relative position model is developed to solve the reference camera position unconstrained problem in the full-parameter calibration model of the OMDPS in accordance with the imaging model of stars and scale bar targets. Subsequently, the problem of adjustment failure and inaccurate adjustment in the multi-data fusion bundle adjustment is solved using the two-norm matrix and the weight matrix to adjust the Jacobian matrix with respect to all system parameters (e.g., camera interior parameters (CIP), camera exterior parameters (CEP), and lens distortion parameters (LDP)). Finally, all system parameters can be optimized simultaneously using this algorithm. In the actual data ground-based experiment, 333 spatial targets are measured using the V-star System (VS) and OMDPS. Taking the measurement of VS as the true value, the measurement results of OMDPS indicated that the in-plane Z-direction target coordinates root-mean-square error (RMSE) is less than 0.0538 mm and the Z-direction RMSE is less than 0.0428 mm. Out-of-plane Y-direction RMSE is less than 0.1514 mm. The application potential of the PG system for on-orbit measurement tasks is demonstrated through the actual data ground-based experiment.

RESECTION AND MONTE CARLO COVARIANCE FROM VANISHING POINTS FOR IMAGES OF UNKNOWN ORIGIN

Abstract. Photogrammetric analysis requires camera metadata (position, attitude, interior orientation, etc.), which is not available for all images. Modern commercial solutions for 3D reconstruction from images typically assume large amounts of purposefully-collected, highly-overlapping imagery. In this article, a system is demonstrated for recovering a 3D scene from images of unknown origin, by marking ground space axes, resecting a camera consistent with the markings, and then using the solved camera to collect 3D measurements. The capability works with close-range (vanishing points) and long-range (parallel axes) imagery. Monte Carlo analysis is used to propagate measurement uncertainty of the vanishing lines to uncertainty of exterior and interior camera parameters, which can then be used to quantify uncertainty of measurements in the 3D scene.

Camera Geometric Calibration Using Dynamic Single-Pixel Illumination With Deep Learning Networks

Traditional methods of geometric camera calibration (GCC) are based on angle measurements (AM) or diffractive optical elements (DOE). However, the AM-based approach has the low accuracy and reliability because the vibration of rotating mechanisms, i.e., turntables and angle measuring accuracy easily influence the calibration accuracy. The DOE failure easily occurs in the calibration process when some micro-apertures are blocked by dust particles. In this paper, a new method for the GCC by means of single pixel illumination in a deep neural network (DNN) is presented. A closed-loop calibration link is composed of a single stimulus input produced by a single pixel generator and a collimator, an uncalibrated camera, and a DNN. The dynamic single-pixel illumination forms the different stimulus input of the DNN at different stimulus time. The synaptic weights (the camera interior parameters) of multilayer perceptron are adjusted until the cost function is minimized. This method is able to avoid the aforementioned shortcoming of conventional calibration methods. This method can be especially used for on-ground calibration of remote sensing cameras but in principle also suitable for on-orbit GCC and other cameras.

Improved Position Error Model for Airborne Hyperspectral Imaging Systems

An improved position error model for airborne hyperspectral imaging systems is proposed to quantify the position errors caused by the deficiencies of instruments and the manual installation of integrated systems. The model is based on a thorough analysis of position error sources, which include uncertainties caused by camera position and altitude, interior camera parameters, gyro stabilized mount, position setting error of the global positioning system antenna, and boresight angle error. An improved position error model that describes these error sources is established based on collinear equations. This model is then expanded into the first-order Taylor series. Finally, the error propagation law is applied to estimate the position errors. The performance of the proposed method is evaluated based on real hyperspectral images of Hymap. Results show that the mean square error of the position error is better than 2.1 pixels, which is almost the same as that of ground truth.

Modelling and calibration of depth-dependent distortion for large depth visual measurement cameras

Lens distortion parameters vary with the distance between the object point and the image plane. We propose an analytical model of depth-dependent distortion for large depth-of-field digital cameras used for high accuracy photogrammetry. Compared with the magnification-dependent model, the proposed one does not need focusing operation during calibration, thus eliminates focusing errors and guarantees the stability of camera interior parameters. Compared with the widely used constant distortion parameter model, the proposed model reduces the maximum distortion variation from 8.0 μm to 0.9 μm at 20 mm radial distance when the depth changes from 2.46 m to 4.51 m for the 35 mm lens, and from 23.0 μm to 3.6 μm when the depth changes from 2.07 m to 4.17 m for the 50 mm lens. Additionally, when applied to photogrammetry bundle adjustment, the proposed model reduces length measurement standard deviation from 0.055 mm to 0.028 mm in a measurement volume of 7.0 m × 3.5 m × 2.5m compared with the constant parameter model.

THE EFFECT OF VARIES CAMERA CALIBRATION FIELDS ON CAMERA PARAMETERS

All photogrammetric applications need good camera parameters for mapping purpose, such as an Unmanned Aerial Vehicle (UAV) that encompassed with camera devices. Simple camera calibration is commonly used in many experiments in order to obtain the camera parameter’s value. In aerial mapping, interior camera parameters value of close-range camera calibration is used to correct the image error. However, the changes of the interior calibration parameters used need to be considered at different heights of mapping. Therefore, this research aims to contribute by analyzing of camera parameters’ changes from three heights using calibration field, and one camera calibration in the laboratory as commonly used. Camera distance heights of 15 metre, 25 metre, 55 metre, and 1.4 metre camera distance in the laboratory. The results show the changes in camera parameter’s value. Hence, value of camera calibration parameters of a camera is considered different and can change depend on the distance (height) of calibration.

Digital image correlation for large deformation applied in Ti alloy compression and tension test

In this paper, digital speckle correlation is used in the measurement of Ti alloy compression and tension test. The key technologies applied in the measurement are discussed in detail, including camera calibration with telephoto lens and digital image correlation in large deformation. Single camera self-calibration algorithm based on photogrammetry is proposed. In the algorithm, the interior parameters of camera are estimated without calibrated object, using the bundle adjustment technique, so the 3-D coordinates of calibration target points are not needed in advance to get a reliable camera calibration result. An updating reference image scheme could be employed to deal with large deformation situation. A large deformation measurement scheme, updating reference image scheme, is proposed in this paper. The un-deformed image is used as reference in correlation at first. Only for extremely large deformation field, in which iteration of correlation is not convergent, the reference image is updated to the image of previous deformed stage. Using this method, not only extremely large deformation can be measured successfully but also the accumulated error could be controlled. The 75mm lens is calibrated in the measurement and compared the result with extensometer and un-calibrated image. Experimental results show that up to 150% tensile deformation and 50% compression deformation can be measured successfully.

Analysis of Camera Parameters Value in Various Object Distances Calibration

In photogrammetric applications, good camera parameters are needed for mapping purpose such as an Unmanned Aerial Vehicle (UAV) that encompassed with non-metric camera devices. Simple camera calibration was being a common application in many laboratory works in order to get the camera parameter's value. In aerial mapping, interior camera parameters' value from close-range camera calibration is used to correct the image error. However, the causes and effects of the calibration steps used to get accurate mapping need to be analyze. Therefore, this research aims to contribute an analysis of camera parameters from portable calibration frame of 1.5 × 1 meter dimension size. Object distances of two, three, four, five, and six meters are the research focus. Results are analyzed to find out the changes in image and camera parameters' value. Hence, camera calibration parameter's of a camera is consider different depend on type of calibration parameters and object distances.

Prediction of Georeferencing Precision of Pushbroom Scanner Images

The precision of georeferencing of pushbroom satellite images without using ground control points is estimated. The study includes what-if scenarios for various precisions of star trackers, GPS receivers, image acquisition timing, interior camera parameters, and digital elevation model (DEM). Moreover, the effects of satellite altitude and sensor positions on the pushbroom array are also taken into account. The probability distribution function of the input parameters is determined by considering the sensor characteristics, environmental effects, and assumptions. The precision of the georeferenced coordinates is estimated by Monte Carlo simulation and differential sensitivity analysis. The analyses provide the most probable georeferenced coordinates and their precision for a certain precision of sensors and imaging geometry. The study is conducted based on the BilSAT orbit and SPOT 5 and Landsat optical system characteristics. An Shuttle Radar Topography Mission (SRTM) DEM and a DEM obtained from stereo images are used in georeferencing.

A Method Based on Two Constraints for Camera Calibration Interior Parameters

In the digital photogrammetry system, camera calibration is an essential part and its accuracy directly affects system accuracy. Because non-metric digital camera interior parameters are the stationary in the measurement process, the camera interior parameters should be accurately calibrated in the laboratory. Considering the lack of general DLT algorithm, we adopt nonlinear model of the camera based on distortion. And two additional constraints are used to accurately calibrate the parameters of the camera. The experimental results are analyzed and compared with the V-STARS system. The results of interior parameters calibration are less than 1 pixel. Test results meet the requirements of industrial digital photogrammetry.