Improve a 3D distance measurement accuracy in stereo vision systems using optimization methods’ approach

• Autorzy: Rodríguez-Quiñonez J. C. , Sergiyenko O. , Flores-Fuentes W. , Rivas-lopez M. , Hernandez-Balbuena D. , Rascón R. , Mercorelli P.

Identyfikatory

DOI

10.1016/j.opelre.2017.03.001

• Języki publikacji: EN

Abstrakty

EN

This paper presents a 3D distance measurement accuracy improvement for stereo vision systems using optimization methods A Stereo Vision system is developed and tested to identify common uncertainty sources. As the optimization methods are used to train a neural network, the resulting equation can be implemented in real time stereo vision systems. Computational experiments and a comparative analysis are conducted to identify a training function with a minimal error performance for such method. The offered method provides a general purpose modelling technique, attending diverse problems that affect stereo vision systems. Finally, the proposed method is applied in the developed stereo vision system and a statistical analysis is performed to validate the obtained improvements.

Słowa kluczowe

EN

stereo vision; optimization methods; distance measurement

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2017
• Tom: Vol. 25, No. 1
• Strony: 24--32
• Opis fizyczny: Bibliogr. 36 poz., il., rys., tab., wykr.

Twórcy

autor

Rodríguez-Quiñonez J. C.

• Facultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juarez S/N, Mexicali, Baja California, CP 21280, Mexico

autor

Sergiyenko O.

• Instituto de Ingeniería, Universidad Autónoma de Baja California, Normal S/N y Blvd. Benito Juárez, Mexicali, Baja California, CP 21280, Mexico

autor

Flores-Fuentes W.

• Facultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juarez S/N, Mexicali, Baja California, CP 21280, Mexico

autor

Rivas-lopez M.

• Instituto de Ingeniería, Universidad Autónoma de Baja California, Normal S/N y Blvd. Benito Juárez, Mexicali, Baja California, CP 21280, Mexico

autor

Hernandez-Balbuena D.

• Facultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juarez S/N, Mexicali, Baja California, CP 21280, Mexico

autor

Rascón R.

• Facultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juarez S/N, Mexicali, Baja California, CP 21280, Mexico

autor

Mercorelli P.

• Institute of Product and Process Information, Leuphana University of Lueneburg Volgershall 1, Lueneburg, Germany

Bibliografia

• [1] L. C. Básaca-Preciado, O. Y. Sergiyenko, J. C. Rodríguez-Quinonez, X. García, V. V. Tyrsa, M. Rivas-Lopez, D. Hernandez-Balbuena, P. Mercorelli, M. Podrygalo, A.Gurko, I. Tabakova, O. Starostenko, Optical 3D laser measurement system for navigation of autonomous mobile robot, Opt. Laser Eng. 54 (2014) 159-169, http://dx.doi.org/10.1016/j.optlaseng.2013.08.005.
• [2] W. Flores-Fuentes, M. Rivas-Lopez, O. Sergiyenko, J. Rodriguez-Quinonez, D. Hernandez-Balbuena, J. Rivera-Castillo, Energy centre detection in light scanning sensors for structural health monitoring accuracy enhancement, IEEE J. Sens. 14 (2014) 2355-2361, http://dx.doi.org/10.1109/JSEN.2014.2310224.
• [3] J. C. Rodríguez-Quinonez, O. Y. Sergiyenko, L. C. B. Preciado, V. V. Tyrsa, A. G. Gurko, M. A. Podrygalo, M. R. Lopez, D. H. Balbuena, Optical monitoring of scoliosis by 3D medical laser scanner, Opt. Laser Eng. 54 (2014) 175-186, http://dx.doi.org/10.1016/j.optlaseng.2013.07.026.
• [4] F. Huang, K.-K. Fehrs, G. Hartmann, R. Klette, Wide-angle vision for road views, Opto-Electron. Rev. 21 (2013) 1-22.
• [5] T. Hachaj, M. Ogiela, Real time area-based stereo matching algorithm for multimedia video devices, Opto-Electron. Rev. 21 (2013) 367-375.
• [6] H. Zhao, Z. Wang, H. Jiang, Y. Xu, C. Dong, Calibration for stereo vision system based on phase matching and bundle adjustment algorithm, Opt. Laser Eng. 68 (2015) 203-213.
• [7] J. Xue, X. Su, A new approach for the bundle adjustment problem with fixed constraints in stereo vision, Optik – Int. J. Light Electron Opt. 123 (2012) 1923-1927, http://dx.doi.org/10.1016/j.ijleo.2011.09.025.
• [8] Z. Wang, D. A. Nguyen, J. Barnes, Recent advances in 3D shape measurement and imaging using fringe projection technique, in: SEM Annual Congress and Exposition on Experimental and Applied Mechanics, Albuquerque, New Mexico, 2009.
• [9] G. Saygili, L. van der Maaten, E. A. Hendriks, Adaptive stereo similarity fusion using confidence measures, Comput. Vis. Image Und. 135 (2015) 95-108, http://dx.doi.org/10.1016/j.cviu.2015.02.005.
• [10] S. Ploumpis, A. Amanatiadis, A. Gasteratos, A stereo matching approach based on particle filters and scattered control landmarks, Image Vision Comput. (2015), http://dx.doi.org/10.1016/j.imavis.2015.04.001.
• [11] Z. Zhu, T. Lin, Y. Piao, S. Chen, Recognition of the initial position of weld based on the image pattern match technology for welding robot, Int. J. Adv. Manuf. Tech. 26 (2005) 784-788, http://dx.doi.org/10.1007/s00170-003-2053-8.
• [12] R. Galego, A. Ortega, R. Ferreira, A. Bernardino, J. Andrade Cetto, J. Gaspar, Uncertainty analysis of the dlt-lines calibration algorithm for cameras with radial distortion, Comput. Vis. Image Und. 140 (2015) 115-126.
• [13] J. Salvi, X. Armangu e, J. Batlle, A comparative review of camera calibrating methods with accuracy evaluation, Pattern recogn. 35 (2002) 1617-1635.
• [14] D. Garcia, J.-J. Orteu, M. Devy, Accurate calibration of a stereovision sensor: Comparison of different approaches, VMV (2000) 25-32.
• [15] L. Mei-Song, M. Ping-Wang, L. Lu, H. Jing-Huan, High precision camera calibration in vision measurement, Opt. Laser Tech. 39 (2007) 1413-1420.
• [16] S. Sheng-Wen, H. Yi-Ping, L. Wei-Song, Accurate linear technique for camera calibration considering lens distortion by solving an eigenvalue problem, Opt. Eng. 32 (1993) 138-149.
• [17] L. N. Smith, M. L. Smith, Automatic machine vision calibration using statistica land neural network methods, Image Vis. Comput. 23 (2005) 887-899.
• [18] R. Klette, A. Koschan, K. Schluns, Computer Vision: Three Dimensional Data from Images, Springer, 1998.
• [19] S. Chao-Ton, C. A. Chang, T. Fang-Chih, Neural networks for precise measurement in computer vision systems, Comput. Ind. 27 (1995) 5-236.
• [20] Q. Memon, S. Khan, Camera calibration and three-dimensional world reconstruction of stereo-vision using neural networks, Int. J. Syst. Sci. 32(2001) 1155-1159.
• [21] C. Zhou, Y. Du, Y. Tang, A high-precision calibration method for stereo vision system, in: A. Bhatti (Ed.), Advances in Theory and Applications of Stereo Vision, InTech, 2011, 2017.
• [22] M. B. Lynch, C. H. Dagli, M. Vallenki, The use of feedforward neural networks for machine vision calibration, Int. J. Prod. Econ. 60–61 (1999) 479-489.
• [23] Y. Do, Application of neural networks for stereo-camera calibration, Neural Networks IEEE Int. Joint Conf. 4 (1999) 2719-2722.
• [24] M. Jalali-Heravi, M. Asadollahi-Baboli, P. Shahbazikhah, Qsar study of heparanase inhibitors activity using artificial neural networks and levenberg-marquardt algorithm, Eur. J. Med. Chem. 43 (2008) 548-556.
• [25] I. Mukherjee, S. Routroy, Comparing the performance of neural networks developed by using levenberg-marquardt and quasi-newton with the gradient descent algorithm for modelling a multiple response grinding process, Expert Syst. Appl. 39 (2012) 2397-2407.
• [26] J. C. Rodriguez-Quinonez, O. Sergiyenko, F. F. Gonzalez-Navarro, L. Basaca-Preciado, V. Tyrsa, Surface recognition improvement in 3D medical laser scanner using levenberg-marquardt method, Signal Process 93 (2013) 378-386.
• [27] J. Rodríguez-Quiñonez, O. Sergiyenko, D. Hernandez-Balbuena, M. Rivas-Lopez, W. Flores-Fuentes, L. Basaca-Preciado, Improve 3D laser scanner measurements accuracy using a ffbp neural network with widrow-hoff weight/bias learning function, Opto-Electron. Rev. 22 (2014) 224-235.
• [28] R. S. Sexton, J. N. Gupta, Comparative evaluation of genetic algorithm and backpropagation for training neural networks, Inf. Sci. 129 (2000) 5-59.
• [29] M. Hayati, Z. Mohebi, Application of artificial neural networks for temperature forecasting World Academy of Science, Eng. Technol. 28 (2007) 275-279.
• [30] E. Guresen, G. Kayakutlu, T. U. Daim, Using artificial neural network models in stock market index prediction, Expert Syst. Appl. 38 (2011) 10389-10397.
• [31] M. Hanke, Conjugate gradient type methods for ill-posed problems, Vol. 327, CRC Press, 1995, 2017, pp. 327.
• [32] J. Nocedal, S. J. Wright, Numerical Optimization, Springer, 1999.
• [33] A. Abba, F. Caponio, A. Geraci, G. Ripamontii, Non-linear least-squares in fpga devices for digital spectroscopy, Nuclear Science Symp. Conf. Record (2009)563-568, Orlando.
• [34] C. Herold, V. Despiegel, S. Gentric, S. Dubuisson, I. Bloch, Recursive head reconstruction from multi-view video sequences, Comput. Vis. Image Und. 122 (2014) 182-201.
• [35] B. M. Wilamowski, H. Yu, Improved computation for Levenberg-Marquardt training, IEEE Trans. Neural Netw. 21 (2010) 930-937.
• [36] D. Marquardt, An algorithm for least-squares estimation of nonlinear parameters, SIAM J. Appl. Math. 11 (1963) 431-441.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)