Precise 3D Reconstruction of Cultural Objects Using Combined Multi-component Image Matching and Active Contours Segmentation

  • Christos Stentoumis
  • Georgios Livanos
  • Anastasios Doulamis
  • Eftychios Protopapadakis
  • Lazaros Grammatikopoulos
  • Michael Zervakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8034)


Cultural and creative industries constitute a large range of economic activities. Towards this expansion we need to state the inclusion of ICT technologies, as such of 3D reconstruction methods. However, precise 3D reconstruction under a computationally affordable manner is a research challenge. One way to precisely reconstruct a cultural object is through the use of photogrammetry with the main goal of finding the correspondences between two or more images to reconstruct 3D surfaces. A cultural object is often surrounded by visual background data that should be excluded to improve 3D reconstruction accuracy. Background conditions dynamically change, especially if the object is captured under outdoor conditions, where many occlusions occur and the shadows effects are not negligible. In this paper, we propose a combine image segmentation and matching method to yield an affordable 3D reconstruction of cultural objects. Image segmentation is performed on the use of active contours while image matching through novel multi-cost criteria optimization functions. Experimental results on real-life ancient column capitals indicate the efficiency of the proposed scheme both in terms of performance efficiency and cost.


Active Contour Stereo Match Support Region Creative Industry Cultural Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Hesmondhalgh, D.: The Cultural Industries. Sage (2002)Google Scholar
  2. 2.
    Yan Cui, S., Schuon, D., Chan, S., Thrun, T.C.: 3D shape scanning with a time-of-flight camera. In: Computer Vision and Pattern Recognition (CVPR), pp. 1173–1180 (2010)Google Scholar
  3. 3.
    Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P.T., Shotton, J., Hodges, S., Freeman, D., Davison, A., Fitzgibbon, A.: KinectFusion: Real-time 3D Recon-struction and Interaction Using a Moving Depth Camera. In: UIST, pp. 559–568 (2011)Google Scholar
  4. 4.
    Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. on PAMI 31(9), 1582–1599 (2009)CrossRefGoogle Scholar
  5. 5.
    Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151–158. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  6. 6.
    Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(4), 401–406 (1998)CrossRefGoogle Scholar
  7. 7.
    Hirschmüller, H.: Stereo processing by semi-global matching and mutual information. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(2), 328–341 (2008)CrossRefGoogle Scholar
  8. 8.
    Stentoumis, C., Grammatikopoulos, L., Kalisperakis, I., Petsa, E., Karras, G.: A local adaptive approach for dense stereo matching in architectural scene reconstruction. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL–5/W1, pp. 219–226 (2013)Google Scholar
  9. 9.
    Tombari, F., Mattoccia, S., Di Stefano, L., Addimanda, E.: Classification and evaluation of cost aggregation methods for stereo correspondence. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  10. 10.
    Yoon, K.J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(4), 650–656 (2006)CrossRefGoogle Scholar
  11. 11.
    Bobick, A.F., Intille, S.S.: Large occlusion stereo. IJCV 33(3), 181–200 (1999)CrossRefGoogle Scholar
  12. 12.
    Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Transactions on Image Processing 16(5), 1395–1411 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, New Jersey (2002), Florida, R.: The Rise of the Creative Class and How It’s Transforming Work, Leisure and Everyday Life. Basic Books (2002)Google Scholar
  14. 14.
    Markovic, D., Gelautz, M.: Experimental Combination of Intensity and Stereo Edges for Improved Snake Segmentation. Pattern Recogn. and Image Analysis 17(1), 131–135 (2007)CrossRefGoogle Scholar
  15. 15.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Journal on Computer Vision 43, 7–27 (2001)Google Scholar
  16. 16.
    Chan, T.F., Vese, L.A.: Active Contours Without Edges. IEEE Transactions on Image Processing 10(2) (2001)Google Scholar
  17. 17.
    Yoon, K.J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(4), 650–656 (2006)CrossRefGoogle Scholar
  18. 18.
    Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Trans. on CSVT 19(7), 1073–1079 (2009)Google Scholar
  19. 19.
    Stentoumis, C., Grammatikopoulos, L., Kalisperakis, I., Karras, G.: Implementing an adaptive approach for dense stereo-matching. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII(5), 309–314 (2012)CrossRefGoogle Scholar
  20. 20.
    Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., Zhang, X.: On building an accurate stereo matching system on graphics hardware. In: Proc. ICCV Workshop on GPU in Computer Vision Applications, pp. 467–474 (2011)Google Scholar
  21. 21.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Conf. on CVPR, pp. I-511–I-518 (2001)Google Scholar
  22. 22.
    Yuille, A.L., Poggio, T.: A generalized ordering constraint for stereo correspondence, MIT, AI Lab., Memo 777 (1984)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christos Stentoumis
    • 1
  • Georgios Livanos
    • 2
  • Anastasios Doulamis
    • 2
  • Eftychios Protopapadakis
    • 2
  • Lazaros Grammatikopoulos
    • 3
  • Michael Zervakis
    • 2
  1. 1.Photogrammatric LabNational Technical University of AthensZografouGreece
  2. 2.Image processing and Computer Vision LabTechnical University of CreteChaniaGreece
  3. 3.Depart. of TopographyTechnological Education Institute of AthensAegaleoGreece

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