-
Loktev Alexey Alexeevich -
Moscow State University of Civil Engineering (MSUCE)
Candidate of Physical and Mathematical Sciences, Associated Professor, Department of Theoretical Mechanics and Aerodynamics, Moscow State University of Civil Engineering (MSUCE), 26 Yaroslavskoe shosse, Moscow, 129337, Russian Federation;
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
.
-
Alfimtsev Alexander Nikolaevich -
Moscow State Technical University named after N.E. Bauman (МSTU)
Candidate of Technical Sciences, Associated Professor, Department of Information Systems and Telecommunications
+7 (499) 267-65-37, Moscow State Technical University named after N.E. Bauman (МSTU), 5 2-nd Baumanskaya st., Moscow, 105005, Russian Federation;
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
.
-
Loktev Daniil Alexeevich -
Moscow State Technical University named after N.E. Bauman (МSTU)
student, Department of Informatics and Control Systems
+7 (499) 267-65-37, Moscow State Technical University named after N.E. Bauman (МSTU), 5 2-nd Baumanskaya st., Moscow, 105005, Russian Federation;
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
.
Comprehensive distributed safety, control, and monitoring systems applied by companies and organizations of different ownership structure play a substantial role in the present-day society.
Video surveillance elements that ensure image processing and decision making in automated or automatic modes are the essential components of new systems. This paper covers the modeling of video surveillance systems installed in buildings, and the algorithm, or pattern, of video camera placement with due account for nearly all characteristics of buildings, detection and recognition facilities, and cameras themselves. This algorithm will be subsequently implemented as a user application.
The project contemplates a comprehensive approach to the automatic placement of cameras that take account of their mutual positioning and compatibility of tasks.
The project objective is to develop the principal elements of the algorithm of recognition of a moving object to be detected by several cameras. The image obtained by different cameras will be processed. Parameters of motion are to be identified to develop a table of possible options of routes. The implementation of the recognition algorithm represents an independent research project to be covered by a different article. This project consists in the assessment of the degree of complexity of an algorithm of camera placement designated for identification of cases of inaccurate algorithm implementation, as well as in the formulation of supplementary requirements and input data by means of intercrossing sectors covered by neighbouring cameras. The project also contemplates identification of potential problems in the course of development of a physical security and monitoring system at the stage of the project design development and testing.
The camera placement algorithm has been implemented as a software application that has already been pilot tested on buildings and inside premises that have irregular dimensions. The algorithm has an operating pattern that may be used to develop an automated system of video surveillance and control for any building. The constituent elements of the system will be interconnected with account for their peculiarities and technical specifications
DOI: 10.22227/1997-0935.2012.5.167 - 175
References
- Nikitin V.V., Tsytsulin A.K. Televidenie v sistemakh fizicheskoy zashchity [Television within the Framework of Systems of Physical Protection: Tutorial]. St.Petersburg, LETI Publ., 2001,135 p.
- Volkhonskiy G.V. Kriterii vybora razreshayushchey sposobnosti v sistemakh telenablyudeniya [Criteria of Choice of Resolution of Videosurveillance Systems]. PROSystem CCTV, 2009, no.2 (38), pp. 60—64.
- Aydarov Yu.R. Novyy algoritm analiza protokolov informatsionnoy bezopasnosti i otsenka ego vychislitel’noy slozhnosti [New Algorithm of Analysis of Protocols of Information Security and Assessment of Its Computational Complexity]. Vestnik Permskogo universiteta. Seriya: Matematika. Mekhanika. Informatika [Proceedings of Perm University. Series: Mathematics. Mechanics. Informatics]. 2008, no. 4, pp. 165—168.
- Kudryavtsev V.B., Andreev A.E. O slozhnosti algoritmov [About the Complexity of Algorithms]. Fundamental’naya i prikladnaya matematika [Fundamental and Applied Mathematics]. 2010, no. 3, vol. 15, pp. 135—181.
- Alfimtsev A.N., Devyatkov V.V. Intellektual’nye mul’timodal’nye interfeysy [Intellectual Multimodal Interfaces]. Kaluga, Poligraf-Inform Publ., 2011, 328 p.
- Devyatkov V.V., Alfimtsev A.N. Raspoznavanie manipulyativnykh zhestov [Recognition of Manipulative Gestures]. Vestnik MGTU im. N.E. Baumana. Ser. Priborostroenie [Proceedings of МSTU im. N.E. Bauman. Series: Instrument Engineering]. 2007, no. 3, pp. 56—75.
- Loktev A.A., Zaletdinov A.V. Opredelenie tochek vzaimodeystviya pryamykh i otrazhennykh voln v plastinke [Identification of Points of Interaction of Direct and Reflected Waves in the Plate]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2010, no. 4, pp. 303—308.
-
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
.
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
- Othonos A., Kalli K. Fiber Bragg Gratings: Fundamentals and Applications in Telecommunications and Sensing. London, Artech House, 1999, 422 p.
- Kraft H., Frey J., Moeller T., Albrecht M., Grothof M., Schink B., Hess H., Buxbaum B. 3D-Camera of High 3D-Frame Rate, Depth-Resolution and Background Light Elimination Based on Improved PMD (Photonic Mixer Device)-Technologies. OPTO 2004. AMA Fachverband, Nürnberg, 2004, pp. 45—49.
- Nielsen C.K., Andersen T.V., Keiding S.R. Stability Analysis of an All-Fiber Coupled Cavity Fabry — Perot Additive Pulse Modelocked Laser. J. Quantum Electronics. 2005, vol. 41, no. 2, pp. 198—204. http://dx.doi.org/10.1109/JQE.2004.839717.
- Akimov D., Vatolin D., Smirnov M. Single-Image Depth Map Estimation Using Blur Information. Proceeding of the 21st GraphiCon International Conference on Computer Graphics and Vision. 2011, pp. 112—116.
- Kuhnert K.-D., Langer M., Stommel M., Kolb A. Dynamic 3D-Vision. Vision Systems: Applications. June 2007, pp. 311—334.
- Churin P., Poddaeva O.I. Aerodynamic Testing of Bridge Structures. Applied Mechanics and Materials. 2014, vol. 467, pp. 404—409.
- 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. 9470—9475. DOI: http://dx.doi.org/10.5220/0003356904840491.
- 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.
- 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.
- 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.
- Kaptelinin V. Activity Theory: Implications for Human-Computer Interaction. Context and Consciousness: Activity Theory and Human-Computer Interaction. B. Nardi (Ed.). Cambridge (MA), MIT Press, 1996, pp. 103—116.
- Cremers D., Soatto S. Motion Competition: A Variational Framework for Piecewise Parametric Motion Segmentation. International Journal of Computer Vision. 2005, vol. 62, no. 3, pp. 249—265. DOI: http://dx.doi.org/10.1007/s11263-005-4882-4.
- 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.
- 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)
- 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)
- 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.
- 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)
- 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.
- 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.
- 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.
- 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.
-
Loktev Alexey Alexeevich -
Moscow State University of Civil Engineering (MSUCE)
Candidate of Physical and Mathematical Sciences, Associated Professor, Department of Theoretical Mechanics and Aerodynamics, Moscow State University of Civil Engineering (MSUCE), 26 Yaroslavskoe shosse, Moscow, 129337, Russian Federation;
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
.
-
Alfimtsev Alexander Nikolaevich -
Moscow State Technical University named after N.E. Bauman (МSTU)
Candidate of Technical Sciences, Associated Professor, Department of Information Systems and Telecommunications
+7 (499) 267-65-37, Moscow State Technical University named after N.E. Bauman (МSTU), 5 2-nd Baumanskaya st., Moscow, 105005, Russian Federation;
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
.
-
Loktev Daniil Alexeevich -
Moscow State Technical University named after N.E. Bauman (МSTU)
student, Department of Informatics and Control Systems
+7 (499) 267-65-37, Moscow State Technical University named after N.E. Bauman (МSTU), 5 2-nd Baumanskaya st., Moscow, 105005, Russian Federation;
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
.
The second important problem to be resolved to the algorithm and its software, that comprises an automatic design of a complex closed circuit television system, represents object recognition, by virtue of which an image is transmitted by the video camera. Since imaging of almost any object is dependent on many factors, including its orientation in respect of the camera, lighting conditions, parameters of the registering system, static and dynamic parameters of the object itself, it is quite difficult to formalize the image and represent it in the form of a certain mathematical model. Therefore, methods of computer-aided visualization depend substantially on the problems to be solved. They can be rarely generalized. The majority of these methods are non-linear; therefore, there is a need to increase the computing power and complexity of algorithms to be able to process the image. This paper covers the research of visual object recognition and implementation of the algorithm in the view of the software application that operates in the real-time mode
DOI: 10.22227/1997-0935.2012.5.194 - 201
References
- Nikitin V.V., Tsytsulin A.K. Televidenie v sistemakh fizicheskoy zashchity [Television within the Framework of Systems of Physical Protection: Tutorial]. St.Petersburg, LETI Publ., 2001,135 p.
- Volkhonskiy G.V. Kriterii vybora razreshayushchey sposobnosti v sistemakh telenablyudeniya [Criteria of Choice of Resolution of Videosurveillance Systems]. PROSystem CCTV, 2009, no.2 (38), pp. 60—64.
- Druki A.A. Sistema poiska, vydeleniya i raspoznavaniya lits na izobrazheniyakh [System of Search, Identification, and Recognition of Faces in Images]. Izvestiya Tomskogo politekhnicheskogo universiteta [News Bulletin of Tomsk Polytechnic University]. 2011, no. 5, vol. 318, pp. 64—70.
- Chernomordik I.V. Ob odnom algoritme vosstanovleniya v zadache raspoznavaniya izobrazheniya [About the Algorithm of Recovery within the Framework of Image Recognition Problem]. Vestnik Permskogo universiteta. Seriya: Matematika. Mekhanika. Informatika [Proceedings of Perm University. Series: Mathematics. Mechanics. Informatics]. 2010, vol. 4(4), pp. 50—53.
- Glumov N.I. Myasnikov E.V., Kopenkov V.N., Chicheva M.A. Metod bystroy korrelyatsii s ispol’zovaniem ternarnykh shablonov pri raspoznavanii ob”ektov na izobrazheniyakh [Method of Fast Correlation Based on the Use of Ternary Patterns as Part of Object Recognition in Images]. Komp’yuternaya optika [Computer Optics]. 2008, no. 3, vol. 32, pp. 277—282.
- Kravchenko P.P., Khusainov N.Sh., Khadzhinov A.A., Pogorelov K.V., Shkurko A.N. Programmnaya sistema mnogostoronnego obmena audiovideoinformatsiey dlya ispol’zovaniya v sistemakh videonablyudeniya [Software System of Multilateral Exchange of Audio Information to Be Implemented in Systems of Video Surveillance]. Informatsionnoe protivodeystvie ugrozam terrorizma [Informational Resistance to Threats of Terrorism]. 2002, no. 1, pp. 109—114.
-
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
.
-
Bakhtin Vadim Fedorovich -
Engineering Center of Technical Examination and Diagnosis “Expert” (ECTED “Expert”)
Head, Department of the Examination of Industrial Safety of Buildings and Structures, Engineering Center of Technical Examination and Diagnosis “Expert” (ECTED “Expert”), 82 Konstruktorov str., Voronezh, 394038, Russian Federation; +7 (473) 2788-991;
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
.
-
Chernikov Igor’ Yur’evich -
Engineering Center of Technical Examination and Diagnosis “Expert” (ECTED “Expert”)
leading specialist, Department for the Examination of Industrial Safety of Buildings and Structures, Engineering Center of Technical Examination and Diagnosis “Expert” (ECTED “Expert”), 82 Konstruktorov str., Voronezh, 394038, Russian Federation; +7 (473) 2788-991;
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
.
The recent decade has been the time of the rapid development of communication infrastructure, but very often the structures erected in the middle of the last century are used as a basis for new transmission units and antennas, which are considerably worn out. In this regard the control problems of the infrastructure facilities such as towers and masts are often emerging. Such tasks may be associated with the test required when installing additional equipment and modules, as well as during the scheduled inspection and certification of individual objects in accordance with the legal documents. Timely detection of critical deformations will to a large extent prevent the occurrence of accidents and disasters. For accurate detection of deformations load cells on the basis of the piezoelectric effect and fiber-optic sensors based on Bragg gratings are most commonly used, but in such distributed information measurement systems there are significant drawbacks, which narrow the scope of their possible application. Among the main disadvantages there are: high cost of initial installation and configuration, and the subsequent operation of such systems. Traditional measuring sensors require power, separate line of measurement information signal, as well as lines for supplying control signals. A significant limitation is that any sensor detects deformation or other parameters of the design only for its whole base, thus, active sensors should be installed in structures, in which an altered state was detected by visual inspection or by other means. The emergence of video and photo-detectors with high resolution and other settings to get a high-quality image of the object made it possible to establish the systems for infrastructure objects’ monitoring with the characteristics acceptable for practice. At the heart of such systems there are not only detectors with high sensitivity, but also the algorithms for the objects’ recognition, determination of their geometrical parameters by analyzing a series of images. This is the issue and the subject of this work, which developed the computational algorithms to detect external defects. At the stage of preliminary image processing there is the delineation of characteristic points in the image and the calculation of the optical flow in the area of these points. When determining the defect position, the characteristic points of the image are determined using the detector of Harris-Laplace, which are located in the central part of the image. The characteristic points outside the frame are considered to be background. There is an identification of the changes in characteristic points in the frame in relation to the background by using a pyramidal iterative scheme. In the second stage servo frame focuses on a specific point with the greatest change in relation to the background in the current time. The algorithm for object detection and determination of its parameters includes three procedures: detection procedure start; the procedure of the next image processing; stop procedure for determining the parameters of the object. The method described here can be used to create information-measuring system of monitoring based on the use of photodetectors with high-definition and recognition of defects (color differences and differences in the form compared to the background). Since almost each examination of a building or structure begins with a visual examination and determination of the most probable places of occurrence and presence of the defects, the proposed method can be combined with this stage and it will simplify the process of diagnosing, screening for the development of projects on reconstruction and placement of additional equipment on the existing infrastructure.
DOI: 10.22227/1997-0935.2015.3.7-16
References
- Othonos A., Kalli K. Fiber Bragg Gratings: Fundamentals and Applications in Telecommunications and Sensing. London, Artech House, 1999, 422 p.
- Ivanov V.S., Kravtsov V.E., Tikhomirov S.V. Problems of Metrological Support of Measurements in Fiber-Optic Transmission Systems. Proc. of SPIE. 2002, vol. 4900, pp. 430—440. DOI: http://dx.doi.org/10.1117/12.484593.
- Nielsen C.K., Andersen T.V., Keiding S.R. Stability Analysis of an All-Fiber Coupled Cavity Fabry-Perot Additive Pulse Mode-locked Laser. J. Quantum Electronics. 2005, vol. 41, no. 2, pp. 198—204. DOI: http://dx.doi.org/10.1109/JQE.2004.839717.
- Bakhtin V.F., Chernikov I.Yu., Loktev A.A. Raschet na dinamicheskoe vozdeystvie machty sotovoy sistemy svyazi i plity perekrytiya, na kotoruyu ona opiraetsya [Analysis of the Dynamic Load Applied to a Cellular Communication Mast and a Ceiling Panel on Which It Rests]. Vestnik MGSU [Proceedings of Moscow State University of Civil Engineering]. 2012, no. 8, pp. 66—75. (In Russian)
- Akimov D., Vatolin D., Smirnov M. Single-Image Depth Map Estimation Using Blur Information. 21st GraphiCon International Conference on Computer Graphics and Vision. Conference Paper. Moscow, 2011, pp. 12—15.
- Churin P., Poddaeva O.I. Aerodynamic Testing of Bridge Structures. Applied Mechanics and Materials. 2014, vol. 477—478, pp. 817—821. DOI: http://dx.doi.org/10.4028/www.scientific.net/AMM.477-478.817.
- 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. 9470—9475. DOI: http://dx.doi.org/10.5220/0003356904840491.
- Loktev A.A. Non-elastic models of interaction of an impactor and an Uflyand — Mindlin Plate. International Journal of Engineering Science. 2012, vol. 50, no. 1, pp. 46—55. DOI: http://dx.doi.org/10.1016/j.ijengsci.2011.09.004.
- Kuhnert K.-D., Langer M., Stommel M., Kolb A. Dynamic 3D-Vision. Vision Systems: Applications. June 2007, pp. 311—334. DOI: http://dx.doi.org/10.5772/4995.
- 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, art. 70, pp. 124—132.
- Nagata T., Koyanagi M., Tsukamoto H., Saeki S., Isono K., Shichida Y., Tokunaga F., Kinoshita M., Arikawa K., Terakita A. Depth Perception from Image Defocus in a Jumping Spider. Science. 2012, vol. 335, no. 6067, pp. 469—471. DOI: http://dx.doi.org/10.1126/science.1211667.
- Bruhn A., Weickert J., SchnEorr C. Lucas-Kanade meets Horn-Schunck: Combining Local and Global Optic Flow Methods. International Journal of Computer Vision. 2005, vol. 61, no. 3, pp. 211—231. DOI: http://dx.doi.org/10.1023/B:VISI.0000045324.43199.43.
- Cremers D., Soatto S. Motion Competition: a Variational Framework for Piecewise Parametric Motion Segmentation. International Journal of Computer Vision. 2005, vol. 62, no. 3, pp. 249—265. DOI: http://dx.doi.org/10.1007/s11263-005-4882-4.
- 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.
- Hahne Uw. Real-time Depth Imaging. Tu Berlin, Fakultät Iv, Computer Graphics, 2012, 108 p.
- Somaiya A.H. High Speed Automatic Depth Map Generation for 3D Television. European Scientific Journal December edition. 2012, vol. 8, no. 30, pp. 127—142.
- 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.
- Kowdle A., Snavely N., Chen T. Recovering Depth of a Dynamic Scene Using Real World Motion Prior. Proceedings of Computer Vision and Pattern Recognition (CVPR). 2011, pp. 14—20. DOI: http://dx.doi.org/10.1109/ICIP.2012.6467083.
- 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.
- 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.