scholarly journals Perspective transformation based-initial value estimation for speckle control points matching in out-of-focus camera calibration using synthetic speckle pattern

2021 ◽  
Author(s):  
Xiao Yang ◽  
Xiaobo Chen ◽  
Juntong Xi
Keyword(s):  
Camera Calibration ◽  
Speckle Pattern ◽  
Perspective Transformation ◽  
Control Points ◽  
Initial Value ◽  
Value Estimation

scholarly journals An Initial Value Estimation Method for the Kalman and Extended Kalman Filters in Underground Metal Detection

2019 ◽  
Vol 9 (19) ◽  
pp. 4113 ◽  
Author(s):  
Yadong Wan ◽  
Zhen Wang ◽  
Peng Wang ◽  
Zhiyang Liu ◽  
Na Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Detection Methods ◽  
Polarizability Tensor ◽  
Filter Method ◽  
Excellent Performance ◽  
Initial Value ◽  
Initial State ◽  
Metal Detection ◽  
Value Estimation

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

scholarly journals AUTOMATIC KAPPA ANGLE ESTIMATION FOR AIR PHOTOS BASED ON PHASE ONLY CORRELATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 131-135
Author(s):  
Z. Xiong ◽  
D. Stanley ◽  
Y. Xin
Keyword(s):  
Bundle Adjustment ◽  
Ground Control ◽  
Correlation Technique ◽  
Control Points ◽  
Initial Value ◽  
Ground Control Points ◽  
Attitude Angle ◽  
Camera Position ◽  
Air Photo ◽  
Orientation Parameters

The approximate value of exterior orientation parameters is needed for air photo bundle adjustment. Usually the air borne GPS/IMU can provide the initial value for the camera position and attitude angle. However, in some cases, the camera’s attitude angle is not available due to lack of IMU or other reasons. In this case, the kappa angle needs to be estimated for each photo before bundle adjustment. The kappa angle can be obtained from the Ground Control Points (GCPs) in the photo. Unfortunately it is not the case that enough GCPs are always available. In order to overcome this problem, an algorithm is developed to automatically estimate the kappa angle for air photos based on phase only correlation technique. This function has been embedded in PCI software. Extensive experiments show that this algorithm is fast, reliable, and stable.

scholarly journals 3D THERMAL MAPPING OF BUILDING ROOFS BASED ON FUSION OF THERMAL AND VISIBLE POINT CLOUDS IN UAV IMAGERY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 271-277
Author(s):  
M. Dahaghin ◽  
F. Samadzadegan ◽  
F. Dadras Javan
Keyword(s):  
Camera Calibration ◽  
Point Cloud ◽  
Point Clouds ◽  
Control Points ◽  
Thermal Camera ◽  
Dense Point ◽  
Normal Vectors ◽  
Projection Error ◽  
Orientation Parameters ◽  
Vegetation Layer

Abstract. Thermography is a robust method for detecting thermal irregularities on the roof of the buildings as one of the main energy dissipation parts. Recently, UAVs are presented to be useful in gathering 3D thermal data of the building roofs. In this topic, the low spatial resolution of thermal imagery is a challenge which leads to a sparse resolution in point clouds. This paper suggests the fusion of visible and thermal point clouds to generate a high-resolution thermal point cloud of the building roofs. For the purpose, camera calibration is performed to obtain internal orientation parameters, and then thermal point clouds and visible point clouds are generated. In the next step, both two point clouds are geo-referenced by control points. To extract building roofs from the visible point cloud, CSF ground filtering is applied, and the vegetation layer is removed by RGBVI index. Afterward, a predefined threshold is applied to the normal vectors in the z-direction in order to separate facets of roofs from the walls. Finally, the visible point cloud of the building roofs and registered thermal point cloud are combined and generate a fused dense point cloud. Results show mean re-projection error of 0.31 pixels for thermal camera calibration and mean absolute distance of 0.2 m for point clouds registration. The final product is a fused point cloud, which its density improves up to twice of the initial thermal point cloud density and it has the spatial accuracy of visible point cloud along with thermal information of the building roofs.

scholarly journals STUDY OF THE INTEGRATION OF LIDAR AND PHOTOGRAMMETRIC DATASETS BY <i>IN SITU</i> CAMERA CALIBRATION AND INTEGRATED SENSOR ORIENTATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 647-652 ◽  
Author(s):  
E. Mitishita ◽  
F. Costa ◽  
M. Martins
Keyword(s):  
Camera Calibration ◽  
Control Points ◽  
Engineering Project ◽  
Integrated Sensor ◽  
Common Procedure ◽  
Sensor Orientation ◽  
The Moment ◽  
High Degree

Photogrammetric and Lidar datasets should be in the same mapping or geodetic frame to be used simultaneously in an engineering project. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. Although the direct sensor orientation technologies provide a high degree of automation process due to the GNSS/INS technologies, the accuracies of the results obtained from the photogrammetric and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to verify the importance of the in situ camera calibration and Integrated Sensor Orientation without control points to increase the accuracies of the photogrammetric and LIDAR datasets integration. The horizontal and vertical accuracies of photogrammetric and Lidar datasets integration by photogrammetric procedure improved significantly when the Integrated Sensor Orientation (ISO) approach was performed using Interior Orientation Parameter (IOP) values estimated from the in situ camera calibration. The horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar check points, increased around of 37% and 198% respectively.

Initial Value Estimation of Digital Image Correlation Method in Deformation Measurement Based on Feature Matching

2020 ◽  
Vol 57 (18) ◽  
pp. 181012
Author(s):  
朱天天 Zhu Tiantian ◽  
付中男 Fu Zhongnan ◽  
张梅 Zhang Mei ◽  
叶沛 Ye Pei ◽  
李桂华 Li Guihua
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
Feature Matching ◽  
Correlation Method ◽  
Deformation Measurement ◽  
Image Correlation ◽  
Initial Value ◽  
Digital Image Correlation Method ◽  
Value Estimation
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