Neural network model for phase-height relationship of each image pixel in 3D shape measurement by machine vision

• Autorzy: Chung B

Identyfikatory

DOI

10.5277/oa140409

• Języki publikacji: EN

Abstrakty

EN

In a three-dimensional measurement system based on a digital light processing projector and a camera, a height estimating function is very important. Sinusoidal fringe patterns of the projector are projected onto the object, and the phase of the measuring point is calculated from the camera image. Then, the height of the measuring point is inferred by the phase. The phase-to-height relationship is unique at each image point. However it is nonlinearly different according to the image coordinates. It is also difficult to obtain the geometrical model because of lens distortion. Even though some studies have been performed on neural network models to find the height from the phase and the related coordinates, the results are not good because of the complex relationship. Therefore, this paper proposes a hybrid method that combines a geometric analysis and a neural network model. The proposed method first finds the phase-to-height relationship from a geometric analysis for each image pixel, and then uses a neural network model to find the related parameters for the relationship. The experimental results show that the proposed method is superior to previous neural network methods.

Słowa kluczowe

EN

machine vision; shape measurement; fringe pattern projection; phase-height relationship; neural network

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2014
• Tom: Vol. 44, nr 4
• Strony: 587--599
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Chung B

• School of Mechanical Engineering, Yeungnam University, Gyeongsan 712-749, Korea
• [e-mail]

Bibliografia

• [1] CHEN F., BROWN G.M., SONG M., Overview of three-dimensional shape measurement using optical methods, Optical Engineering 39(1), 2000, pp. 10–22.
• [2] YINGSONG HU, JIANGTAO XI, ENBANG LI, JOE CHICHARO, ZONGKAI YANG, Three-dimensional profilometry based on shift estimation of projected fringe patterns, Applied Optics 45(4), 2006, pp. 678–687.
• [3] WEI-HUNG SU, KEBIN SHI, ZHIWEN LIU, BO WANG, REICHARD K., SHIZHUO YIN, A large-depth-of-field projected fringe profilometry using supercontinuum light illumination, Optics Express 13(3), 2005, pp. 1025–1032.
• [4] SANSONI G., CAROCCI M., RODELLA R., Three-dimensional vision based on a combination of gray--code and phase-shift light projection: analysis and compensation of the systematic errors, Applied Optics 38(31), 1999, pp. 6565–6573.
• [5] FEIPENG DA, SHAOYAN GAI, Flexible three-dimensional measurement technique based on a digital light processing projector, Applied Optics 47(3), 2008, pp. 377–385.
• [6] YI-BAE CHOI, SEUNG-WOO KIM, Phase-shifting grating projection Moire topography, Optical Engineering 37(3), 1998, pp. 1005–1010.
• [7] LEI HUANG, CHUA P.S.K., ASUNDI A., Least-squares calibration method for fringe projection profilometry considering camera lens distortion, Applied Optics 49(9), 2010, pp. 1539–1548.
• [8] HONGYU LIU, WEI-HUNG SU, REICHARD K., SHIZHUO YIN, Calibration-based phase-shifting projected fringe profilometry for accurate absolute 3D surface profile measurement, Optics Communications 216(1–3), 2003, pp. 65–80.
• [9] TAKEDA M., MUTOH K., Fourier transform profilometry for the automatic measurement of 3D object shapes, Applied Optics 22(24), 1983, pp. 3977–3982.
• [10] QINGYING HU, PEISEN S. HUANG, QIONGLIN FU, FU-PEN CHIANG, Calibration of a three-dimensional shape measurement system, Optical Engineering 42(2), 2003, pp. 487–493.
• [11] MAURE A., COBELLI P., PAGNEUX V., PETITJEANS P., Experimental and theoretical inspection of the phase-to-height relation in Fourier transform profilometry, Applied Optics 48(2), 2009, pp. 380–392.
• [12] HUA DU, ZHAOYANG WANG, Three-dimensional shape measurement with an arbitrarily arranged fringe projection profilometry system, Optics Letters 32(16), 2007, pp. 2438–2440.
• [13] HONGWEI GUO, HAITAO HE, YINGJIE YU, MINGYI CHEN, Least-squares calibration method for fringe projection profilometry, Optical Engineering 44(3), 2005, article 033603.
• [14] WENGUO LI, SUPING FANG, SHAOJUN DUAN, 3D shape measurement based on structured light projection applying polynomial interpolation technique, Optik – International Journal for Light and Electron Optics 124(1), 2013, pp. 20–27.
• [15] CUEVAS F., SERVIN M., RODRIGUEZ-VERA R., Depth object recovery using radial basis functions, Optics Communications 163(4–6), 1999, pp. 270–277.
• [16] CUEVAS F., SERVIN M., STAVROUDIS O., RODRIGUEZ-VERA R., Multi-layer neural network applied to phase and depth recovery from fringe patterns, Optics Communications 181(4–6), 2000, pp. 239–259.
• [17] DINESH GANOTRA, JOBY JOSEPH, KEHAR SINGH, Profilometry for the measurement of three-dimensional object shape using radial basis function, and multi-layer perceptron neural networks, Optics Communications 209(4–6), 2002, pp. 291–301.
• [18] YAN TANGY, WEN-JING CHEN, XIAN-YU SU, LI-QUN XIANG, Neural network applied to reconstruction of complex objects based on fringe projection, Optics Communications 278(2), 2007, pp. 274–278.
• [19] BYEONG-MOOK CHUNG, YOON-CHANG PARK, Hybrid method for phase-to-height relationship in 3D shape measurement using fringe pattern projection, International Journal of Precision Engineering and Manufacturing 15(3), 2014, pp. 407–413.
• [20] MARQUARDT D., An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics 11, 1963, pp. 431–441.