Abstract
This chapter addresses the problem of feature-based 3D reconstruction model for close-range objects. Since it is almost impossible to find pixel-to-pixel correspondences from 2D images by algorithms when the object is imaged on a close range, the selection of feature correspondences, as well as the number and distribution of them, play important roles in the reconstruction accuracy. Then, features on representative objects are analyzed and discussed. The impact of the number and distribution of feature correspondences is analyzed by reconstructing an object with standard cylinder shape by following the reconstruction model introduced in this chapter. After that, three criteria are set to guide the selection of feature correspondences for more accurate 3D reconstruction. These criteria are finally applied to the human finger since it is a typical close-range object and different number and distribution of feature correspondences can be established automatically from its 2D fingerprints. The effectiveness of the setting criteria is demonstrated by comparing the accuracy of reconstructed finger shape based on different fingerprint feature correspondences with the corresponding 3D point cloud data obtained by structured light illumination (SLI) technique which is taken as a ground truth in this chapter.
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References
Rusinkiewicz, S., Hall-Holt, O., Levoy, M.: Real-time 3D model acquisition. ACM Trans. Graph. (TOG). 21(3), 438–446 (2002)
Bradley, B.D., Chan, A.D.C, Hayes, M.J.D:. A simple, low cost, 3D scanning system using the laser light-sectioning method. 2008 IEEE Instrumentation and Measurement Technology Conference. IEEE (2008)
Wu, Q., et al.: A 3D modeling approach to complex faults with multi-source data. Comput. Geosci. 77, 126–137 (2015)
Wang, Y., Hassebrook, L.G., Lau, D.L.: Data acquisition and processing of 3-D fingerprints. IEEE Trans. Inf. Forensics Secur. 5(4), 750–760 (2010)
Stockman, G.C., et al.: Sensing and recognition of rigid objects using structured light. IEEE Control. Syst. Mag. 8(3), 14–22 (1988)
Hu, G., Stockman, G.: 3-D surface solution using structured light and constraint propagation. IEEE Trans. Pattern Anal. Mach. Intell. 11(4), 390–402 (1989)
Horn Berthold, K.P.: Shape from shading: A Method for Obtaining the Shape of a Smooth Opaque Object from One View (1970)
Zhang, R., et al.: Shape-from-shading: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 690–706 (1999)
Worthington, P.L.: Reillumination-driven shape from shading. Comput. Vis. Image Underst. 98(2), 325–343 (2005)
Akimoto, T., Suenaga, Y., Wallace, R.S.: Automatic creation of 3D facial models. IEEE Comput. Graph. Appl. 13(5), 16–22 (1993)
Lao, S., et al.: Building 3D facial models and detecting face pose in 3D space. Second International Conference on 3-D Digital Imaging and Modeling (Cat. no. PR00062). IEEE (1999)
Brodsky, T., Fermuller, C., Aloimonos, Y.: Shape from video. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. no PR00149), vol. 2. IEEE (1999)
Strecha, C., et al.: Shape from video vs still images. Proc. Opti. 3D Meas. Tech. 2, 168–175 (2003)
Woodham, R.J.: Photometric method for determining surface orientation from multiple images. Opt. Eng. 19(1), 191139 (1980)
Rushmeier, H., Taubin, G., Guéziec, A.: Applying Shape from Lighting Variation to Bump Map Capture Rendering techniques, vol. 97, pp. 35–44. Springer, Vienna (1997)
Malzbender, T., et al.: Surface enhancement using real-time photometric stereo and reflectance transformation. Rendering techniques 2006: 17th (2006)
Grün, A.: Semi-automated approaches to site recording and modeling. IAPRS. 33, 309–318 (2000)
Bradley, B.D., Chan, A.D.C., Hayes, M.J.D.: A simple, low cost, 3D scanning system using the laser light-sectioning method. 2008 IEEE Instrumentation and Measurement Technology Conference. IEEE (2008)
Paris, S.: Methods for 3D Reconstruction from Multiple Images. Cambridge, Massachussets Institute of Technology. [online] Disponible en https://people.csail.mit.edu/sparis/talks/Paris_06_3D_Reconstruction.pdf. Consultado el 29.06(2006) (2018)
Said, A.Md, Hasbullah, H., Baharudin, B.: Image-based modeling: A review. J. Theor. Appl. Inf. Technol. 5(2) (2009)
Remondino, F., Hakim, S.-E.: Image-based 3D modelling: A review. Photogramm. Rec. 21(115), 269–291 (2006)
Udayan, J.D., Kim, H.S., Kim, J.-I.: Animage-based approach to the reconstruction of ancient architectures by extracting and arranging 3D spatial components. Front. Inf. Technol. Electron. Eng. 16(1), 12–27 (2015)
Grün, A., Remondino, F., Zhang, L.: Photogrammetric reconstruction of the great Buddha of Bamiyan, Afghanistan. Photogramm. Rec. 19(107), 177–199 (2004)
Zhu, S., Xia, Y.: 3D Simulation and Reconstruction of Large-Scale Ancient Architecture with Techniques of Photogrammetry and Computer Science (2005)
Zhang, R., et al.: Shape-from-shading: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 690–706 (1999)
Poggio, G.F., Poggio, T.: The analysis of stereopsis. Annu. Rev. Neurosci. 7(1), 379–412 (1984)
Hartley, R.I., Mundy, J.L.: Relationship between photogrammmetry and computer vision. In: Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision, vol. 1944. International Society for Optics and Photonics (1993)
Henrichsen, Arne. 3D Reconstruction and Camera Calibration from 2D Images. Dissertations. University of Cape Town, (2000)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge university press, New York (2003)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
Bouguet, J.: First calibration example – corner extraction, calibration, additional tools. Camera Calibration Toolbox for Matlab. www.vision.caltech.edu/bouguetj/calib_doc/htmls/example.html
Correspondence Problem. Wikipedia, Wikimedia Foundation, 8 Oct.2019. http://en.wikipedia.org/wiki/Correspondence_problem#Basic_Method
Ogale, A.S., Aloimonos, Y.: Shape and the stereo correspondence problem. Int. J. Comput. Vis. 65(3), 147–162 (2005)
Belhumeur, P.N., Mumford, D.: A Bayesian treatment of the stereo correspondence problem using half-occluded regions. CVPR. 506, 512 (1992)
Liu, F., et al.: Touchless multiview fingerprint acquisition and mosaicking. IEEE Trans. Instrum. Meas. 62(9), 2492–2502 (2013)
Wang, Y., Hassebrook, L.G., Lau, D.L.: Data acquisition and processing of 3-D fingerprints. IEEE Trans. Inf. Forensics Secur. 5(4), 750–760 (2010)
Zhang, D., et al.: Robust palmprint verification using 2D and 3D features. Pattern Recogn. 43(1), 358–368 (2010)
Choi, H., Choi, K., Kim, J.: Mosaicing touchless and mirror-reflected fingerprint images. IEEE Trans. Inf. Forensics Secur. 5(1), 52–61 (2010)
Jain, A., Hong, L., Bolle, R.: On-line fingerprint verification. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 302–314 (1997)
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Liu, F., Zhao, Q., Zhang, D. (2020). 3D Fingerprint Generation. In: Advanced Fingerprint Recognition: From 3D Shape to Ridge Detail. Springer, Singapore. https://doi.org/10.1007/978-981-15-4128-5_3
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DOI: https://doi.org/10.1007/978-981-15-4128-5_3
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