The Influence of Camera and Optical System Parameters on the Uncertainty of Object Location Measurement in Vision Systems

The article presents the influence of the camera and its optical system on the uncertainty of object position measurement in vision systems. The aim of the article is to present the methodology for estimating the combined standard uncertainty of measuring the object position with a vision camera treated as a measuring device. The identification of factors affecting the location measurement uncertainty and the determination of their share in the combined standard uncertainty will allow determining the parameters of the camera operation, so that the expanded uncertainty is as small as possible in the given measurement conditions. The analysis of the uncertainty estimation presented in the article was performed with the assumption that there is no influence of any external factors (e.g., temperature, humidity, or vibrations).

[1]  Fabiana Rodrigues Leta,et al.  Computer vision measurement system for standards calibration in XY plane with sub-micrometer accuracy , 2019, The International Journal of Advanced Manufacturing Technology.

[2]  Paul R. Schrater,et al.  Handling shape and contact location uncertainty in grasping two-dimensional planar objects , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Wenzhong Shi,et al.  Accuracy Assessment Measures for Object Extraction from Remote Sensing Images , 2018, Remote. Sens..

[4]  V. Cioban,et al.  Image calibration for color comparison , 2012, Proceedings of 2012 IEEE International Conference on Automation, Quality and Testing, Robotics.

[5]  Marcin Zych,et al.  Radiometric methods in the measurement of particle-laden flows , 2017 .

[6]  Jonghoon Park,et al.  State estimation with delayed measurements considering uncertainty of time delay , 2009, 2009 IEEE International Conference on Robotics and Automation.

[7]  Miklos Sajben,et al.  Uncertainty Estimates for Pressure Sensitive Paint Measurements , 1993 .

[8]  V. V. Myansikov,et al.  The accuracy dependency investigation of simultaneous localization and mapping on the errors from mobile device sensors , 2019 .

[9]  Keith M. Chugg,et al.  Machine Learning Based Image Calibration for a Twofold Time-Interleaved High Speed DAC , 2019, 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS).

[10]  Christopher G. Relf,et al.  Image Acquisition and Processing with LabVIEW , 2003 .

[11]  Carl D. Meinhart,et al.  Recent Advances in Micro-Particle Image Velocimetry , 2010 .

[12]  Thomas Spiegel,et al.  AC-DC current transfer step-up and step-down calibration and uncertainty calculation , 2002, IEEE Trans. Instrum. Meas..

[13]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Zhigang Liu,et al.  A High-Precision Detection Approach for Catenary Geometry Parameters of Electrical Railway , 2017, IEEE Transactions on Instrumentation and Measurement.

[16]  Alessio Carullo,et al.  Uncertainty issues in the experimental assessment of degradation rate of power ratings in photovoltaic modules , 2017 .

[17]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[18]  M. Jaszczur,et al.  Application of Laser Induced Fluorescence in experimental analysis of convection phenomena , 2016 .

[19]  Jovan Bojkovski,et al.  Methods of reducing the uncertainty of the self-heating correction of a standard platinum resistance thermometer in temperature measurements of the highest accuracy , 2003 .

[20]  M. Mizan,et al.  Monitoring of current collectors on the railway line , 2016 .

[21]  J. Westerweel,et al.  Particle Image Velocimetry for Complex and Turbulent Flows , 2013 .

[22]  S. Standard.  GUIDE TO THE EXPRESSION OF UNCERTAINTY IN MEASUREMENT , 2006 .

[23]  X. Zhang,et al.  Quantification of Extensional Uncertainty of Segmented Image Objects by Random Sets , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Qin Li,et al.  Calibration of Three CCD Camera Overhead Contact Line Measuring System , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[26]  Jacek Skibicki,et al.  The issue of uncertainty of visual measurement techniques for long distance measurements based on the example of applying electric traction elements in diagnostics and monitoring , 2018 .

[27]  Christoph H. Lampert,et al.  Document image dewarping using robust estimation of curled text lines , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[28]  Mita Nasipuri,et al.  Multistage Curvilinear Coordinate Transform Based Document Image Dewarping using a Novel Quality Estimator , 2020, ArXiv.

[29]  M. Marron Romera,et al.  Non-contact sensor for monitoring catenary-pantograph interaction , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[30]  Sayantan Bhattacharya,et al.  Volumetric particle tracking velocimetry (PTV) uncertainty quantification , 2019, Experiments in Fluids.

[31]  R. Hanus,et al.  Determination of the uncertainty of mass flow measurement using the orifice for different values of the Reynolds number , 2019, EPJ Web of Conferences.

[32]  E. R. Cohen.  An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements , 1998 .

[33]  Volodymyr Mosorov,et al.  Density and velocity determination for single-phase flow based on radiotracer technique and neural networks , 2018, Flow Measurement and Instrumentation.

[34]  Jiarui Cui,et al.  An Improved Pose Estimation Method Based on Projection Vector With Noise Error Uncertainty , 2019, IEEE Photonics Journal.

[35]  D. Louis Collins,et al.  Multi-Modal Image Registration Based on Gradient Orientations of Minimal Uncertainty , 2012, IEEE Transactions on Medical Imaging.

[36]  Ioannis Pratikakis,et al.  A Methodology for Document Image Dewarping Techniques Performance Evaluation , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[37]  Neena Valecha,et al.  Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria , 2015, Malaria Journal.

[38]  Brian S Thurow,et al.  Uncertainty characterization of particle location from refocused plenoptic images. , 2017, Optics express.

[39]  Anna Golijanek-Jędrzejczyk,et al.  Estimation of the uncertainty of the LEM CV 3-500 transducers conversion function , 2015 .

[40]  Leon Swędrowski,et al.  UNCERTAINTY ANALYSIS OF MEASURING SYSTEM FOR INSTANTANEOUS POWER RESEARCH , 2012 .

[41]  Xin Zheng,et al.  Two-dimensional spectral image calibration based on feed-forward neural network , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[42]  Uncertainty in Electrical Measurements , 2016 .

[44]  Ralph Etienne-Cummings,et al.  FPGA emulation of a spike-based, stochastic system for real-time image dewarping , 2015, 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS).

[45]  António Araújo.  Dual-band pyrometry for emissivity and temperature measurements of gray surfaces at ambient temperature: The effect of pyrometer and background temperature uncertainties , 2016 .

[46]  Leszek Jarzebowicz,et al.  Analysis of Measurement Errors in Rail Vehicles’ Pantograph Inspection System , 2016 .

[47]  Guifeng Zhang,et al.  Uncertainty analysis of object location in multi-source remote sensing imagery classification , 2009 .

[49]  Andreas Dengel,et al.  Document Image Dewarping using Deep Learning , 2019, ICPRAM.

[50]  V. Dolz,et al.  Uncertainties in power computations in a turbocharger test bench , 2015 .