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A super depth of field height measurement based on local disparity

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A super depth of field height measurement method is proposed to measure the object height with the optical stereoscopic microscope. The quasi-Euclidean epipolar rectification algorithm is utilized on the original stereoimage to obtain rectified stereoimages and calibrate two camera parameters. Then, feature points are obtained by the SURF (speed up robust feature) algorithm and their corresponding disparities are calculated. The disparity-depth of field curve is fitted by combining the step height values of a stepper motor. Moreover, through local disparity value got from feature points on the object, the relative shift height is calculated through regression analysis. Finally, according to binocular vision geometry, the thickness of the object can be calculated. Experimental results show that the measurement error in Z direction is from 1.51% to 7.71%, which indicates that the proposed method is able to measure the height of a microobject beyond depth of field within a tolerant error.
Czasopismo
Rocznik
Strony
205--214
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
autor
  • Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
autor
  • Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
autor
  • Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
autor
  • Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
autor
  • Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
Bibliografia
  • [1] ZHENXING HU, HUIYANG LUO, YINGJIE DU, HONGBING LU, Fluorescent stereo microscopy for 3D surface profilometry and deformation mapping, Optics Express 21(10), 2013, pp. 11808–11818.
  • [2] OANCEA R., VASILE L., MARCHESE C., SAVA-ROSIANU R., Stereomicroscopic study of the human tooth caries - clinical and morphological correlations, Proceedings of SPIE 8427, 2012, article 842740.
  • [3] LEE M.P., GIBSON G.M., PHILLIPS D., PADGETT M.J., TASSIERI M., Dynamic stereo microscopy for studying particle sedimentation, Optics Express 22(4), 2014, pp. 4671–4677.
  • [4] JUNG HYUN KIM, Visually guided 3D micro positioning and alignment system, International Journal of Precision Engineering and Manufacturing 12(5), 2011, pp. 797–803.
  • [5] ADELSON E.H., WANG J.Y.A., Single lens stereo with a plenoptic camera, IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 1992, pp. 99–106.
  • [6] LEVIN A., FERGUS R., DURAND F., FREEMAN W.T., Image and depth from a conventional camera with a coded aperture, ACM Transactions on Graphics (TOG) 26(3), 2007, article 70.
  • [7] GALLO A., MUZZUPAPPA M., BRUNO F., 3D reconstruction of small sized objects from a sequence of multi-focused images, Journal of Cultural Heritage 15(2), 2014, pp. 173–182.
  • [8] IKHYUN LEE, MUHAMMAD TARIQ MAHMOOD, TAE-SUN CHOI, Adaptive window selection for 3D shape recovery from image focus, Optics and Laser Technology 45, 2013, pp. 21–31.
  • [9] SHAOJIE ZHUO, SIM T., Defocus map estimation from a single image, Pattern Recognition 44(9), 2011, pp. 1852–1858.
  • [10] TARKAN AYDIN, YUSUF SINAN AKGUL, A New Adaptive Focus Measure for Shape From Focus, BMVC, 2008.
  • [11] YIBIN TIAN, Autofocus using image phase congruency, Optics Express 19(1), 2011, pp. 261–270.
  • [12] FUSIELLO A., IRSARA L., Quasi-Euclidean epipolar rectification of uncalibrated images, Machine Vision and Applications 22(4), 2011, pp. 663–670.
  • [13] BAY H., ESS A., TUYTELAARS T., VAN GOOL L., SURF: speeded up robust features, Computer Vision and Image Understanding (CVIU) 110(3), 2008, pp. 346–359.
  • [14] MUJA M., LOWE D.G., Fast approximate nearest neighbors with automatic algorithm configuration, [In] International Conference on Computer Vision Theory and Applications (VISAPP ’09), 2009.
  • [15] FISCHLER M.A., BOLLES R.C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM 24(6), 1981, pp. 381–395.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2e715f05-9055-4ff9-8869-1284451e1e90
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