scholarly journals Marker-Based Structural Displacement Measurement Models with Camera Movement Error Correction Using Image Matching and Anomaly Detection

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5676
Author(s):  
Jisung Kim ◽  
Youngdo Jeong ◽  
Hyojin Lee ◽  
Hongsik Yun
Keyword(s):  
Anomaly Detection ◽  
Error Correction ◽  
Image Matching ◽  
Systematic Errors ◽  
Displacement Measurement ◽  
Outdoor Environment ◽  
Construction Sites ◽  
Conventional Model ◽  
Measurement Models ◽  
Movement Error

To prevent collapse accidents at construction sites, the marker-based displacement measurement method was developed. However, it has difficulty in obtaining accurate measurements at long distances (>50 m) in an outdoor environment because of camera movements. To overcome this problem, marker-based structural displacement measurement models using image matching and anomaly detection were designed in this study. Then, the performance of each model in terms of camera movement error correction was verified through comparison with that of a conventional model. The results show that the systematic errors due to camera movements (<1.7°) were corrected. The detection rate of markers with displacement reached 95%, and the probability that the error size would be less than 10 mm was ≥ 95% with a 95% confidence interval at a distance of more than 100 m. Moreover, the normalized mean square error was less than 0.1. The models developed in this study can measure the pure displacement of an object without the systematic errors caused by camera movements. Furthermore, these models can be used to measure the displacements of distant structures using closed-circuit television cameras and markers in an outdoor environment with high accuracy.

scholarly journals Anomaly Detection of Trackside Equipment Based on GPS and Image Matching

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 17346-17355
Author(s):  
Limin Gao ◽  
Yunyang Jiu ◽  
Xiang Wei ◽  
Zhongchuan Wang ◽  
Weiwei Xing
Keyword(s):  
Anomaly Detection ◽  
Image Matching

scholarly journals Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

2020 ◽  
Vol 12 (4) ◽  
pp. 696 ◽  
Author(s):  
Zhen Ye ◽  
Yusheng Xu ◽  
Hao Chen ◽  
Jingwei Zhu ◽  
Xiaohua Tong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Comparative Study ◽  
Image Matching ◽  
Systematic Errors ◽  
Disparity Estimation ◽  
Sensing Applications ◽  
Subpixel Accuracy ◽  
Dense Image ◽  
Remote Sensing Applications ◽  
Practical Analysis

Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

scholarly journals Systematic Error Correction of Dynamical Seasonal Prediction of Sea Surface Temperature Using a Stepwise Pattern Project Method

2008 ◽  
Vol 136 (9) ◽  
pp. 3501-3512 ◽  
Author(s):  
Jong-Seong Kug ◽  
June-Yi Lee ◽  
In-Sik Kang
Keyword(s):  
Statistical Model ◽  
Systematic Error ◽  
Error Correction ◽  
Statistical Error ◽  
Temporal Correlation ◽  
Systematic Errors ◽  
Climate Prediction ◽  
Global Ocean ◽  
Coupled Gcm ◽  
Mean State

Abstract Every dynamical climate prediction model has significant errors in its mean state and anomaly field, thus degrading its performance in climate prediction. In addition to correcting the model’s systematic errors in the mean state, it is also possible to correct systematic errors in the predicted anomalies by means of dynamical or statistical postprocessing. In this study, a new statistical model has been developed based on the pattern projection method in order to empirically correct the dynamical seasonal climate prediction. The strength of the present model lies in the objective and automatic selection of optimal predictor grid points. The statistical model was applied to systematic error correction of SST anomalies predicted by Seoul National University’s (SNU) coupled GCM and evaluated in terms of temporal correlation skill and standardized root-mean-square error. It turns out that the statistical error correction improves the SST prediction over most regions of the global ocean with most forecast lead times up to 6 months. In particular, the SST predictions over the western Pacific and Indian Ocean are improved significantly, where the SNU coupled GCM shows a large error.

Self-Calibration of Coupling Error for 3-DOF Displacement Measurement of Planar Working Stage Based on Two Planar Gratings

2017 ◽  
Vol 870 ◽  
pp. 135-140
Author(s):  
Yong Meng Liu ◽  
Ze Lin Li ◽  
De Hao Du ◽  
Mao Qiang Yuan ◽  
Jing Zhi Huang ◽  
...  
Keyword(s):  
Fourier Series ◽  
Calibration Method ◽  
Systematic Errors ◽  
Displacement Measurement ◽  
The Self ◽  
Experimental Results ◽  
Positioning Accuracy ◽  
Self Calibration ◽  
Coupling Error

A self-calibration method of coupling error is presented for 3-DOF displacement measurement of a planar moving stage based on two planar gratings. The self-calibration method using Fourier series is developed to extract the periodic systematic errors from the coupling errors. The extracted periodic systematic errors are compensated. Experiments are conducted to validate the validity of the self-calibration method and experimental results indicate that the coupling errors in x and y directions are reduced by 2 and 1.5 times respectively. It can be therefore concluded that the self-calibration method is suitable for the 3-DOF displacement measurement of a planar moving stage to improve the positioning accuracy.

scholarly journals Methods for the calibrated measurement of the scattering parameters of planar multi-port devices

2007 ◽  
Vol 5 ◽  
pp. 439-445 ◽  
Author(s):  
I. Rolfes ◽  
B. Schiek
Keyword(s):  
Error Correction ◽  
Measurement System ◽  
Systematic Errors ◽  
Microwave Circuits ◽  
Calibration Procedure ◽  
Vector Network Analyzer ◽  
Switching Network ◽  
Network Analyzer ◽  
Scattering Parameters

Abstract. In this article, the error-corrected determination of complex scattering parameters of multi-port devices by means of a 2-port vector network analyzer is presented. As only two ports of the device under test can be connected to the analyzer ports at a time, the remaining device ports have to be terminated by external reflections. In order to measure the scattering parameters of the DUT without the influence of systematic errors and of the external terminations, an error correction has to be performed besides the calibration. For this purpose, the application of the multi-port procedure is presented. This method has the advantage, that the external reflective terminations can be chosen arbitrarily. Furthermore, these terminations can be unknown except for one. An automatized measurement system based on a switching network is shown, which is optimized for the measurement of planar microwave circuits. An error model for the description of the measurement setup as well as a calibration procedure for the elimination of the systematic errors are presented.

scholarly journals Random Finite Set-Based Anomaly Detection for Safety Monitoring in Construction Sites

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 105710-105720 ◽  
Author(s):  
Ammar Mansoor Kamoona ◽  
Amirali Khodadadian Gostar ◽  
Ruwan Tennakoon ◽  
Alireza Bab-Hadiashar ◽  
David Accadia ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Safety Monitoring ◽  
Construction Sites ◽  
Random Finite Set ◽  
Finite Set

scholarly journals Least squares image matching: A comparison of the performance of robust estimators

2014 ◽  
Vol II-1 ◽  
pp. 37-44 ◽  
Author(s):  
Z. Li ◽  
J. Wang
Keyword(s):  
Least Squares ◽  
Image Matching ◽  
Ground Truth ◽  
Real Data ◽  
Systematic Errors ◽  
Performance Comparison ◽  
Estimation Methods ◽  
Robust Estimators ◽  
Practical Applications ◽  
Imaging Sensor

Least squares image matching (LSM) has been extensively applied and researched for high matching accuracy. However, it still suffers from some problems. Firstly, it needs the appropriate estimate of initial value. However, in practical applications, initial values may contain some biases from the inaccurate positions of keypoints. Such biases, if high enough, may lead to a divergent solution. If all the matching biases have exactly the same magnitude and direction, then they can be regarded as systematic errors. Secondly, malfunction of an imaging sensor may happen, which generates dead or stuck pixels on the image. This can be referred as outliers statistically. Because least squares estimation is well known for its inability to resist outliers, all these mentioned deviations from the model determined by LSM cause a matching failure. To solve these problems, with simulation data and real data, a series of experiments considering systematic errors and outliers are designed, and a variety of robust estimation methods including RANSACbased method, M estimator, S estimator and MM estimator is applied and compared in LSM. In addition, an evaluation criterion directly related to the ground truth is proposed for performance comparison of these robust estimators. It is found that robust estimators show the robustness for these deviations compared with LSM. Among these the robust estimators, M and MM estimator have the best performances.

scholarly journals High-Quality Genomes of Nanopore Sequencing by Homologous Polishing

2020 ◽  
Author(s):  
Yao-Ting Huang ◽  
Po-Yu Liu ◽  
Pei-Wen Shih
Keyword(s):  
Neural Network ◽  
Error Correction ◽  
Error Rate ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Systematic Errors ◽  
Nanopore Sequencing ◽  
High Quality ◽  
Microbial Genomes ◽  
Homologous Sequences

AbstractNanopore sequencing has been widely used for reconstruction of a variety of microbial genomes. Owing to the higher error rate, the assembled genome requires further error correction. Existing methods erase many of these errors via deep neural network trained from Nanopore reads. However, quite a few systematic errors are still left on the genome. This paper proposed a new model trained from homologous sequences extracted from closely-related genomes, which provides valuable features missed in Nanopore reads. The developed program (called Homopolish) outperforms the state-of-the-art Racon/Medaka and MarginPolish/HELEN pipelines in metagenomic and isolates of bacteria, viruses and fungi. When Homopolish is combined with Medaka or with HELEN, the genomes quality can exceed Q50 on R9.4 flowcells. The genome quality can be also improved on R10.3 flowcells (Q50-Q90). We proved that Nanopore-only sequencing can now produce high-quality genomes without the need of Illumina hybrid sequencing.

scholarly journals Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites

2020 ◽  
Vol 10 (7) ◽  
pp. 2305
Author(s):  
Mohsen Foroughi Sabzevar ◽  
Masoud Gheisari ◽  
James Lo
Keyword(s):  
Image Matching ◽  
Three Dimensional ◽  
Scene Recognition ◽  
Video Data ◽  
Image Transformation ◽  
Construction Sites ◽  
3D Objects ◽  
Matching Process ◽  
Matching Techniques ◽  
Image Transformations

Image matching techniques offer valuable opportunities for the construction industry. Image matching, a fundamental process in computer vision, is required for different purposes such as object and scene recognition, video data mining, reconstruction of three-dimensional (3D) objects, etc. During the image matching process, two images that are randomly (i.e., from different position and orientation) captured from a scene are compared using image matching algorithms in order to identify their similarity. However, this process is very complex and error prone, because pictures that are randomly captured from a scene vary in viewpoints. Therefore, some main features in images such as position, orientation, and scale of objects are transformed. Sometimes, these image matching algorithms cannot correctly identify the similarity between these images. Logically, if these features remain unchanged during the picture capturing process, then image transformations are reduced, similarity increases, and consequently, the chances of algorithms successfully conducting the image matching process increase. One way to improve these chances is to hold the camera at a fixed viewpoint. However, in messy, dusty, and temporary locations such as construction sites, holding the camera at a fixed viewpoint is not always feasible. Is there any way to repeat and retrieve the camera’s viewpoints during different captures at locations such as construction sites? This study developed and evaluated an orientation and positioning approach that decreased the variation in camera viewpoints and image transformation on construction sites. The results showed that images captured while using this approach had less image transformation in contrast to images not captured using this approach.

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