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INFORMATION SYSTEMS AND LOGISTICS IN CIVIL ENGINEERING

Method of determining the distance to the object by analyzing its image blur

Vestnik MGSU 6/2015
  • Loktev Aleksey Alekseevich - Moscow State University of Civil Engineering (MGSU) Doctor of Physical and Mathematical Sciences, Associate Professor, Department of Theoretical Mechanics and Aerodynamics, Moscow State University of Civil Engineering (MGSU), 26 Yaroslavskoe shosse, Moscow, 129337, Russian Federation; +7 (499) 183-24-01; This e-mail address is being protected from spambots. You need JavaScript enabled to view it .
  • Loktev Daniil Alekseevich - Bauman Moscow State Technical University (BMSTU) postgraduate student, Department of Information Systems and Telecommunications, Bauman Moscow State Technical University (BMSTU), 5 2-ya Baumanskaya str., Moscow, 105005, Russian Federation; This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

Pages 140-151

In modern integrated monitoring systems and systems of automated control of technological processes there are several essential algorithms and procedures for obtaining primary information about an object and its behavior. The primary information is characteristics of static and moving objects: distance, speed, position in space etc. In order to obtain such information in the present work we proposed to use photos and video detectors that could provide the system with high-quality images of the object with high resolution. In the modern systems of video monitoring and automated control there are several ways of obtaining primary data on the behaviour and state of the studied objects: a multisensor approach (stereovision), building an image perspective, the use of fixed cameras and additional lighting of the object, and a special calibration of photo or video detector.In the present paper the authors develop a method of determining the distances to objects by analyzing a series of images using depth evaluation using defocusing. This method is based on the physical effect of the dependence of the determined distance to the object on the image from the focal length or aperture of the lens. When focusing the photodetector on the object at a certain distance, the other objects both closer and farther than a focal point, form a spot of blur depending on the distance to them in terms of images. Image blur of an object can be of different nature, it may be caused by the motion of the object or the detector, by the nature of the image boundaries of the object, by the object’s aggregate state, as well as by different settings of the photo-detector (focal length, shutter speed and aperture).When calculating the diameter of the blur spot it is assumed that blur at the point occurs equally in all directions. For more precise estimates of the geometrical parameters determination of the behavior and state of the object under study a statistical approach is used to determine the individual parameters and estimate their accuracy. A statistical approach is used to evaluate the deviation of the dependence of distance from the blur from different types of standard functions (logarithmic, exponential, linear). In the statistical approach the evaluation method of least squares and the method of least modules are included, as well as the Bayesian estimation, for which it is necessary to minimize the risks under different loss functions (quadratic, rectangular, linear) with known probability density (we consider normal, lognormal, Laplace, uniform distribution). As a result of the research it was established that the error variance of a function, the parameters of which are estimated using the least squares method, will be less than the error variance of the method of least modules, that is, the evaluation method of least squares is more stable. Also the errors’ estimation when using the method of least squares is unbiased, whereas the mathematical expectation when using the method of least modules is not zero, which indicates the displacement of error estimations. Therefore it is advisable to use the least squares method in the determination of the parameters of the function.In order to smooth out the possible outliers we use the Kalman filter to process the results of the initial observations and evaluation analysis, the method of least squares and the method of least three standard modules for the functions after applying the filter with different coefficients.

DOI: 10.22227/1997-0935.2015.6.140-151

References
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  8. Lelegard L., Vallet B., Bredif M. Multiscale Haar Transform for Blur Estimation from a Set of Images. International Archives of Photogrammetry : Remote Sensing and Spatial Information Science. Munich, Germany, October 5—7, 2011, pp. 65—70.
  9. Lin H.-Y., Chang C.-H. Depth from Motion and Defocus Blur. Optical Engineering. December 2006, vol. 45 (12), no. 127201, pp. 1—12. DOI: http://dx.doi.org/10.1117/1.2403851.
  10. Levin A., Fergus R., Durand Fr., Freeman W.T. Image and Depth from a Conventional Camera with a Coded Aperture. ACM Transactions on Graphics. 2007, vol. 26, no. 3, article 70, pp. 124—132.
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  13. Elder J.H., Zucker S.W. Local Scale Control for Edge Detection and Blur Estimation. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1998, vol. 20, no. 7, pp. 120—127.
  14. Alfimtsev A.N., Loktev D.A., Loktev A.A. Razrabotka pol’zovatel’skogo interfeysa kompleksnoy sistemy videomonitoringa [Development of a User Interface for an Integrated System of Video Monitoring]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2012, no. 11, pp. 242—252. (In Russian)
  15. Alfimtsev A.N., Loktev D.A., Loktev A.A. Sravnenie metodologiy razrabotki sistem intellektual’nogo vzaimodeystviya [Comparison of Development Methodologies for Systems of Intellectual Interaction]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2013, no. 5, pp. 200—208. (In Russian)
  16. Jiwani M.A., Dandare S.N. Single Image Fog Removal Using Depth Estimation Based on Blur Estimation. International Journal of Scientific and Research Publications. 2013, vol. 3, no. 6, pp. 1—6.
  17. Loktev A.A., Alfimtsev A.N., Loktev D.A. Algoritm raspoznavaniya ob”ektov [Algorithm of Object Recognition]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2012, no. 5, pp. 194—201. (In Russian)
  18. Robinson Ph., Roodt Yu., Nel A. Gaussian Blur Identification Using Scale-Space Theory. Faculty of Engineering and Built Environment. University of Johannesburg, South Africa, 2007, pp. 68—73.
  19. Wang H., Cao F., Fang Sh., Yang Cao, Fang Ch. Effective Improvement for Depth Estimated Based on Defocus Images. Journal of Computers. April 2013, vol. 8, no. 4, pp. 888—895. DOI: http://dx.doi.org/10.4304/jcp.8.4.888-895.
  20. Trifonov A.P., Korchagin Yu.E., Trifonov M.V., Chernoyarov O.V., Artemenko A.A. Amplitude Estimate of the Radio Signal with Unknown Duration and Initial Phase. Applied Mathematical Sciences. 2014, vol. 8, no. 111, pp. 5517—5528. DOI: http://dx.doi.org/10.12988/ams.2014.47588.
  21. Chernoyarov O.V., Sai Si Thu Min, Salnikova A.V., Shakhtarin B.I., Artemenko A.A.Application of the Local Markov Approximation Method for the Analysis of Information Processes Processing Algorithms with Unknown Discontinuous Parameters. Applied Mathematical Sciences. 2014, vol. 8, no. 90, pp. 4469—4496. DOI: http://dx.doi.org/10.12988/ams.2014.46415.

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EVALUATION OF MEASUREMENTS OF THE DISTANCE TO THE OBJECT IN THE STUDY OF ITS GRAPHIC IMAGE

Vestnik MGSU 10/2015
  • Loktev Aleksey Alekseevich - Moscow State University of Civil Engineering (National Research University) (MGSU) Doctor of Physical and Mathematical Sciences, Professor, Department of Theoretical Mechanics and Aerodynamics, Moscow State University of Civil Engineering (National Research University) (MGSU), 26 Yaroslavskoe shosse, Moscow, 129337, Russian Federation.
  • Loktev Daniil Alekseevich - Bauman Moscow State Technical University (BMSTU) postgraduate student, Department of Information Systems and Telecommunications, Bauman Moscow State Technical University (BMSTU), 5 2-ya Baumanskaya str., Moscow, 105005, Russian Federation; This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

Pages 54-65

An important element of modern automated systems of management, monitoring and control of remote access modules is information about the state and behavior of a static or moving object. In many existing and planned monitoring systems processing graphical image of the object is used, which is obtained by the photo detectors and, thus, the possibility of determining geometric and kinematic parameters of a moving object is significantly reduced due to various aspects of image acquisition, one of such aspects is blur. In the present work, algorithms of the primary information processing obtained on the basis of the graphic image study of a movable or stationary object are improved using the methods and procedures of statistical analysis that allow approximating theoretical results to experimental results. The use of statistical analysis and probabilistic approach increase the accuracy of the determined characteristics, applicability of calculation procedures of the state parameters (size, shape, distance from the observer) and behavior of the object (speed and direction) and reduce the computational complexity of the final algorithm. The Bayesian estimation was obtained based on the use of quadratic, rectangular and simple loss function under normal, Laplace, uniform and lognormal distribution of errors, which allow drawing conclusions about the intervals of various models and algorithms to determine the parameters of different objects. When using the statistical approach it is taken into account that the errors are random in nature and may be considered a variety of probability density functions (normal, lognormal, Laplace and uniform distributions to minimize risks under different loss functions (quadratic, rectangular, linear), and then evaluated using the method of least squares, method of least modules and the Bayesian approach. When performing the evaluation the properties of unbiased, consistency and efficiency are important. Sustainable procedure should have the following properties: for the selected model, the procedure should be close to optimum efficiency; the results should be close to nominal, calculated for the adopted model; the effect of large errors must be eliminated. We use the minimax method of Huber, which assumes that the best estimate will not be worse than in the case of the “least favorable” density distribution. Decision rule is based on the definition of such density that minimizes the information of Fischer that is the variance function of the contribution of the sample. In the present study we offer the procedure of finding a theoretical function based on the assumption that the errors are subjected to the known laws of distribution: normal (Gaussian), Laplacian, uniform, lognormal. This is a significant advantage of the proposed methodology compared to the one used in the previous works of the authors, it is proposed here to use the Bayesian estimation of the measurements as unknown theoretical function that needs to be obtained closer to the observational measurements.

DOI: 10.22227/1997-0935.2015.10.54-65

References
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  6. Gaspar T., Oliveira P. New Dynamic Estimation of Depth from Focus in Active Vision Systems. Preprints of the 18th IFAC World Congress Milano (Italy) August 28 — September 2. 2011, pp. 484—491. DOI: http://dx.doi.org/10.5220/0003356904840491.
  7. Lelegard L., Vallet B., Bredif M. Multiscale Haar Transform for Blur Estimation from a Set of Images. International Archives of Photogrammetry : Remote Sensing and Spatial Information Science. Munich, Germany, October 5—7, 2011, pp. 65—70.
  8. Lin H.-Y., Chang C.-H. Depth from Motion and Defocus Blur. Optical Engineering. December 2006, vol. 45 (12), no. 127201, pp. 1—12. DOI: http://dx.doi.org/10.1117/1.2403851.
  9. Levin A., Fergus R., Durand Fr., Freeman W.T. Image and Depth from a Conventional Camera with a Coded Aperture. ACM Transactions on Graphics. 2007, vol. 26, no. 3, article 70, pp. 124—132.
  10. Loktev A.A., Loktev D.A. Metod opredeleniya rasstoyaniya do ob”ekta putem analiza razmytiya ego izobrazheniya [Method of Determining the Distance to the Object by Analyzing its Image Blur]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2015, no. 6, pp. 140—151. (In Russian)
  11. Sizikov V.S., Rimskikh M.V., Mirdzhamolov R.K. Reconstructing Blurred Noisy Images Without Using Boundary Conditions. Journal of Optical Technology. 2009, vol. 76, no. 5, pp. 279—285. DOI: http://dx.doi.org/10.1364/JOT.76.000279.
  12. Elder J.H., Zucker S.W. Local Scale Control for Edge Detection and Blur Estimation. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1998, vol. 20, no. 7, pp. 699—716. DOI: http://dx.doi.org/10.1109/34.689301.
  13. Alfimtsev A.N., Loktev D.A., Loktev A.A. Razrabotka pol’zovatel’skogo interfeysa kompleksnoy sistemy videomonitoringa [Development of a User Interface for an Integrated System of Video Monitoring]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2012, no. 11, pp. 242—252. (In Russian)
  14. Alfimtsev A.N., Loktev D.A., Loktev A.A. Sravnenie metodologiy razrabotki sistem intellektual’nogo vzaimodeystviya [Comparison of Development Methodologies for Systems of Intellectual Interaction]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2013, no. 5, pp. 200—208. (In Russian)
  15. Jiwani M.A., Dandare S.N. Single Image Fog Removal Using Depth Estimation Based on Blur Estimation. International Journal of Scientific and Research Publications. 2013, vol. 3, no. 6, pp. 1—6.
  16. Loktev A.A., Alfimtsev A.N., Loktev D.A. Algoritm raspoznavaniya ob”ektov [Algorithm of Object Recognition]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2012, no. 5, pp. 194—201. (In Russian)
  17. Robinson Ph., Roodt Yu., Nel A. Gaussian Blur Identification Using Scale-Space Theory. Faculty of Engineering and Built Environment. University of Johannesburg, South Africa, 2007, pp. 68—73.
  18. Langley P. User Modeling in Adaptive Interfaces. Proc. of the Seventh Intern. Conf. on User Modeling. 1997, pp. 357—370.
  19. Trifonov A.P., Korchagin Yu.E., Trifonov M.V., Chernoyarov O.V., Artemenko A.A. Amplitude Estimate of the Radio Signal with Unknown Duration and Initial Phase. Applied Mathematical Sciences. 2014, vol. 8, no. 111, pp. 5517—5528. DOI: http://dx.doi.org/10.12988/ ams.2014.47588.
  20. Chernoyarov O.V., Sai Si Thu Min, Salnikova A.V., Shakhtarin B.I., Artemenko A.A.Application of the Local Markov Approximation Method for the Analysis of Information Processes Processing Algorithms with Unknown Discontinuous Parameters. Applied Mathematical Sciences. 2014, vol. 8, no. 90, pp. 4469—4496. DOI: http://dx.doi.org/10.12988/ ams.2014.46415.

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