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Shape-from-shading using sensor and physical object characteristics applied to human teeth surface reconstruction

Shape-from-shading using sensor and physical object characteristics applied to human teeth surface reconstruction

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Image formation involves understanding the sensors characteristics and object reflectance. In dentistry, for example an accurate three-dimensional (3D) representation of the human jaw may be used for diagnostic and treatment purposes. Photogrammetry can offer a flexible, cost-effective solution in that regard. Nonetheless there are several challenges, such as non-friendly image acquisition environment inside the human mouth, problems with lighting (specularity effects because of saliva, gum discolourisation, and occlusion because of the tongue in the lower jaw), and errors because of the data acquisition sensors (e.g. camera calibration errors, lens distortion and so on). In this study, the authors focus on the 3D surface reconstruction aspect for human jaw modelling based on physical surface characteristics and sensor properties. Owing to apparent lens distortion imposed by near-field imaging, the authors propose a new flexible calibration for lens radial distortion based on a single image of a sphere. The authors propose a non-Lambertian shape-from-shading (SFS) algorithm under perspective projection which benefits from camera calibration parameters. Our experiments provide quantitative metric results for the proposed approach. The reflectance of the tooth surface is modelled by the Oren–Nayar reflectance model for rough surfaces whose roughness parameter is physically computed from an optical surface profiler measurements. As compared to state-of-the-art SFS approaches, our approach is able to recover geometric details of tooth occlusal surface. This work is fundamental for establishing an optical-based approach for reconstructing the human jaw, that is inexpensive and does not use ionising radiation.

References

    1. 1)
      • 1. Laurendeau, D., Possart, D.: ‘A computer-vision technique for the acquisition and processing of 3d profiles of dental imprints: An application in orthodontics’, IEEE Trans. Med. Imaging, 1991, 10, pp. 453461 (doi: 10.1109/42.97596).
    2. 2)
      • 2. Goshtasby, A.A., Nambala, S., deRijk, W.G., Campbell, S.D.: ‘A system for digital reconstruction of gypsum dental casts’, IEEE Trans. Med. Imaging, 1997, 16, pp. 664674 (doi: 10.1109/42.640757).
    3. 3)
      • 3. Grenness, M.J., Osborn, J.E., Tyas, M.J.: ‘Mapping tooth surface loss with a fixed-base stereo-camera’, Photogramm. Rec., 2008, 23, pp. 194207 (doi: 10.1111/j.1477-9730.2008.00479.x).
    4. 4)
      • 4. Mitchell, H.L., Chadwick, R.G.: ‘Challenges of photogrammetric intra-oral tooth measurement’. The Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Kyoto, Japan, 2008, pp. 779782.
    5. 5)
      • 5. Yamany, S.M., Farag, A.A., Tasman, D., Farman, A.G.: ‘A 3-d reconstruction system for the human jaw using a sequence of optical images’, IEEE Trans. Med. Imaging, 2000, 19, pp. 538547 (doi: 10.1109/42.870264).
    6. 6)
      • 6. Ahmed, M.T., Eid, A., Farag, A.A.: ‘Human jaw reconstruction: New approach and improvements’. Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'01), The Netherlands, October 2001.
    7. 7)
      • 7. Horn, B.K.P., Brooks, M.J.: ‘The variational approach to shape from shading’, Comput. Vis. Graph. Image Process., 1986, 33, pp. 174208 (doi: 10.1016/0734-189X(86)90114-3).
    8. 8)
      • 8. Collins, T., Bartoli, A.: ‘Towards live monocular 3d laparoscopy using shading and specularity information’, in Abolmaesumi, P., Joskowicz, L., Navab, N., Jannin, P. (Eds.): ‘Information processing in computer-assisted interventions’ (Springer Berlin Heidelberg, 2012) (LNCS, 7330), pp. 1121.
    9. 9)
      • 9. Zhang, R., Tsai, P., Cryer, J.E., Shah, M.: ‘Shape from shading: A survey’, IEEE Trans. Pattern Anal. Mach. Intell., 1999, 21, (1), pp. 690706 (doi: 10.1109/34.784284).
    10. 10)
      • 10. Durou, J.D., Falcone, M., Sagona, M.: ‘Numerical methods for shape-from-shading: A new survey with benchmarks’, Comput. Vis. Image Underst., 2008, 109, (1), pp. 2243 (doi: 10.1016/j.cviu.2007.09.003).
    11. 11)
      • 11. Courteille, F., Crouzil, A., Durou, J.-D., Gurdjos, P.: ‘Shape from shading for the digitization of curved documents’, J. Math. Imaging Vis., 2007, 18, pp. 301316.
    12. 12)
      • 12. Hasegawa, J.K., Tozzi, C.L.: ‘Shape from shading with perspective projection and camera calibration’, Comput. Graph., 1996, 20, (3), pp. 351364 (doi: 10.1016/0097-8493(96)00004-0).
    13. 13)
      • 13. Prados, E., Camilli, F., Faugeras, O.: ‘A unifying and rigorous shape from shading method adapted to realistic data and applications’, J. Math. Imaging Vis., 2006, 25, pp. 307328 (doi: 10.1007/s10851-006-6899-x).
    14. 14)
      • 14. Tankus, A., Sochen, N., Yeshurun, Y.: ‘Shape-from-shading under perspective projection’, Int. J. Comput. Vis., 2005, 63, (1), pp. 2143 (doi: 10.1007/s11263-005-4945-6).
    15. 15)
      • 15. Yuen, S.Y., Tsui, Y.Y., Chow, C.K.: ‘A fast marching formulation of perspective shape from shading under frontal illumination’, Pattern Recognit. Lett., 2007, 28, pp. 806824 (doi: 10.1016/j.patrec.2006.11.008).
    16. 16)
      • 16. Okatani, T., Deguchi, K.: ‘Shape reconstruction from an endoscope image by shape from shading techniques for a point light source at the projection center’, Comput. Vis. Image Underst., 1997, 66, (2), pp. 119131 (doi: 10.1006/cviu.1997.0613).
    17. 17)
      • 17. Ahmed, A., Farag, A.: ‘Shape from shading under various imaging conditions’. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR'07), Minneapolis, MN, 18-23 June 2007, pp. X1X8.
    18. 18)
      • 18. Oren, M., Nayar, S.: ‘Generalization of Lambert's reflectance model’. Computer Graphics, 28 (Annual Conf. Series), 1994, pp. 239246.
    19. 19)
      • 19. Ragheb, H., Hancock, E.: ‘Surface normals and height from non-Lambertian image data’. Second Int. Symp. on 3D Data Processing, Visualization and Transmission (3DPVT'04), Greece, 2004.
    20. 20)
      • 20. Frankot, R., Chellappa, R.: ‘A method for enforcing integrability in shape from shading algorithms’, IEEE Trans. PAMI, 1988, 10, (4), pp. 439451 (doi: 10.1109/34.3909).
    21. 21)
      • 21. Samaras, D., Metaxas, D.: ‘Incorporating illumination constraints in deformable models for shape from shading and light direction estimation’, IEEE Trans. PAMI, 2003, 25, (2), pp. 247264 (doi: 10.1109/TPAMI.2003.1177155).
    22. 22)
      • 22. Carter, C.N., Pusateri, R.J., Chen, D., Ahmed, A.H., Farag, A.A.: ‘Shape from shading for hybrid surfaces as applied to tooth reconstruction’. Int. Conf. on Image Processing (ICIP10), Hong Kong, 2010, pp. 40494052.
    23. 23)
      • 23. Abdelrahim, A.S., Abdelrahman, M.A., Abdelmunim, H., Farag, A., Miller, M.: ‘Novel image-based 3d reconstruction of the human jaw using shape from shading and feature descriptors’. Proc. of the British Machine Vision Conf., 2011, pp. 41.141.11.
    24. 24)
      • 24. Weng, J., Cohen, P., Herniou, M.: ‘Camera calibration with distortion models and accuracy evaluation’, IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 1992, 14, (10), pp. 965980 (doi: 10.1109/34.159901).
    25. 25)
      • 25. Ahmed, M.T., Farag, A.: ‘Nonmetric calibration of camera lens distortion: Differential methods and robust estimation’, IEEE Trans. Image Process., 2005, 14, (8), pp. 12151230 (doi: 10.1109/TIP.2005.846025).
    26. 26)
      • 26. Robert, L.: ‘Camera calibration without feature extraction’, Comput. Vis. Image Underst., 1996, 63, (2), pp. 314325 (doi: 10.1006/cviu.1996.0021).
    27. 27)
      • 27. Liang, C.K., Shih, Y.C., Chen, H.H.: ‘Light field analysis for modeling image formation’, IEEE Trans. Image Process., 2011, 20, (2), pp. 446460 (doi: 10.1109/TIP.2010.2063036).
    28. 28)
      • 28. Horn, B.: ‘Robot vision’ (McGraw-Hill, 1986).
    29. 29)
      • 29. Horn, B.K.P., Sjoberg, R.W.: ‘Calculating the reflectance map’, Appl. Opt., 1979, 18, pp. 17701779 (doi: 10.1364/AO.18.001770).
    30. 30)
      • 30. Devernay, F., Faugeras, O.: ‘Straight lines have to be straight: Automatic calibration and removal of distortion from scenes of structured environments’, Mach. Vis. Appl., 2001, 1, pp. 1424 (doi: 10.1007/PL00013269).
    31. 31)
      • 31. Swaminathan, R., Nayar, S.: ‘Non-metric calibration of wide-angle lenses and polycameras’, PAMI, 2000, 22, (10), pp. 11721178 (doi: 10.1109/34.879797).
    32. 32)
      • 32. Ricolfe-Viala, C., Sánchez-Salmerón, A.-J.: ‘Robust metric calibration of non-linear camera lens distortion’, Pattern Recognit., 2010, 43, pp. 16881699 (doi: 10.1016/j.patcog.2009.10.003).
    33. 33)
      • 33. Zhang, H., Wong, K., Zhang, G.: ‘Newblock camera calibration from images of spheres’, IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 2007, 29, (3), pp. 499502 (doi: 10.1109/TPAMI.2007.45).
    34. 34)
      • 34. Aloimonos, J.: ‘Perspective approximations’, Image Vis. Comput., 1990, 8, (3), pp. 179192 (doi: 10.1016/0262-8856(90)90064-C).
    35. 35)
      • 35. Zheng, Q., Chellapa, R.: ‘Estimation of illuminant direction, albedo and shape from shading’, IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 1991, 13, (7), pp. 680702 (doi: 10.1109/34.85658).
    36. 36)
      • 36. Slama, C.C.: ‘Manual of photogrammetry’ (American Society of Photogrammetry, 1980).
    37. 37)
      • 37. Pentland, A.P.: ‘Local shading analysis’, IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 1984, PAMI-6, (2), pp. 170187 (doi: 10.1109/TPAMI.1984.4767501).
    38. 38)
      • 38. Nayar, S.K., Ikeuchi, K., Kanade, T.: ‘Surface reflection: physical and geometrical perspectives’, Pattern Anal. Mach. Intell., IEEE Trans., 1991, 13, (7), pp. 611634 (doi: 10.1109/34.85654).
    39. 39)
      • 39. Bennett, J.M., Mattsson, L.: ‘Introduction to surface roughness and scattering’ (Optical Society of America, Washington, DC, 1999).
    40. 40)
      • 40. Fuller, J.L., Denehy, G.E., Schulein, T.M.: ‘Concise dental anatomy and morphology’ (University of Iowa Pubns Dept, 2001, 4th edn.).
    41. 41)
      • 41. Marino, R.C.: ‘Dental anatomy: quick study academic’ (Barcharts Inc, 2004), illustrated by: Vincent Perez.
    42. 42)
      • 42. Blinn, J.F.: ‘Models of light reflection for computer synthesized pictures’, SIGGRAPH Comput. Graph., 1977, 11, pp. 192198 (doi: 10.1145/965141.563893).
    43. 43)
      • 43. Cook, R.L., Torrance, K.E.: ‘A reflectance model for computer graphics’, ACM Trans.Graph., 1982, 1, (1), pp. 724 (doi: 10.1145/357290.357293).
    44. 44)
      • 44. Ashikmin, M., Premoze, S., Shirley, P.: ‘A microfacet-based brdf generator’. Proceedings of the 27th annual conference on Computer graphics and interactive techniques, SIGGRAPH’00, New York, NY, USA, 2000, pp. 6574.
    45. 45)
      • 45. Oren, M., Nayar, S.: ‘Generalization of the Lambertian model and implications for machine vision’, Int. J. Comput. Vis., 1995, 14, (3), pp. 227251 (doi: 10.1007/BF01679684).
    46. 46)
      • 46. Torrance, K.E., Sparrow, E.M.: ‘Theory for off-specular reflection from roughened surfaces’, J. Am. Opt. Soc., 1967, 57, (9), pp. 11051114 (doi: 10.1364/JOSA.57.001105).
    47. 47)
      • 47. Leclerc, Y.G., Bobick, A.F.: ‘The direct computation of height from shading’ (IEEE Computer Sco. Press, 1991), vol. 91, pp. 552558.
    48. 48)
      • 48. Tsai, P.S., Shah, M.: ‘A fast linear shape from shading’. Proc. IEEE Computer Vision and Pattern Recognition Conf. (CVPR), Urbana, Illinois, June 1992, pp. 734736.
    49. 49)
      • 49. Abdelrehim, A.H., Farag, A.A.: ‘A new formulation for shape from shading for non-Lambertian surfaces’. Int. Conf. on Computer Vision and Pattern Recognition CVPR06, NY, USA, 2006, pp. 18171824.
    50. 50)
      • 50. Elad, M., Tal, A., Ar, S.: ‘Content based retrieval of vrml objects: an iterative and interactive approach’. Proc. of the Sixth Eurographics workshop on Multimedia 2001, New York, NY, USA, 2002, pp. 107118..
    51. 51)
      • 51. Chui, H., Rangarajan, A.: ‘A new point matching algorithm for non-rigid registration’, Comput. Vis. Image Underst., 2003, 89, pp. 114141 (doi: 10.1016/S1077-3142(03)00009-2).
    52. 52)
      • 52. Tyrrell Rockafellar, R., Wets, R.J.-B.: ‘Variational analysis’ (Springer-Verlag, 2005), p. 117.
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