Occluded Object Reconstruction with Partial Appearance

Authors(2) :-Thi Thi Soe, Zarni Sann

Objects in a scene are often occluded each other. Reconstructing their appearance from their visible parts plays an important role in object detection, image analysis, scene understanding, and depth estimation. This paper introduces an integrated approach that utilizes inpainting and image segmentation for reconstructing the appearance of occluded objects. In the first stage of the system, inpainting or filling-in the removing region is done by the patch-based method of replicating the remaining color information of an image. Localized region-based Active Contour Model (LRACM), segmentation method is applied to segment the visible parts of the occluded object from the additional image frame in the second stage. Finally, the extracted visible parts are composited onto the inpainted background scene obtained from the first stage. We demonstrate the proposed algorithm with a number of still images. Experimental results show that the reconstructed scenes are natural and plausible like real view.

Authors and Affiliations

Thi Thi Soe
Faculty of Computer Science, Computer University (Mandalay), Myanmar
Zarni Sann
Faculty of Computer System and Technology, Computer University (Mandalay), Myanmar

Inpainting, Segmentation, LRACM, Occluded Object Reconstruction

  1. S. Walden, “The Ravished Image” St. Martin’s Press, New York, 1985.
  2. M. Kass, A. Witkin and D. Terzopoulos. (1987) “Snake : Active Contour Model,” International Journal of Computer Vision. v. 1, n. 4, pp. 321-331
  3. A. Yezzi, A. Tsai, and A. Willsky. (2002) “A fully global approach to image segmentation via coupled curve evolution equations”. Journal of Visual Communication and Image Representation, 13:195216, 2002.
  4. T. F. Chan, J. Shen.(2002) “Mathematical models for local non texture inpaintings” SIAM Journal on Applied Mathematics, 62(3): pp. 10191043, 2002.
  5. S. Esedoglu, J. Shen. (2002) “Digital Inpainting Based on the Mumford-Shah-Euler Image Model,” European Journal of Applied Mathematics, 13, pp. 353 - 370, 2002.
  6. S. Grover, S. Gupta, A. K. Sarje, Ankush. (2005) “A Unified Approach for Digital Image Inpainting Using Bounded Search Space” ICGST International Journal on Graphics, Vision and Image Processing, GVIP, Vol.05, 2005
  7. T. Chan, L. Vese. (2001) “Active contours without edges”. IEEE Transaction on Image Processing 10: 266277. doi:10.1109/83.902291
  8. A. Vasilevskiy, K. Siddiqi. (2002) “Flux maximizing geometric flows”. IEEE Transactions on Pattern Analysis and Machine Intelligence 24: 15651578. doi:10.1109/TPAMI.2002.1114849
  9. M. Bertalmio, L. Vese, G. Sapiro, and S. Osher. (2003) “Simultaneous structure and texture image inpainting” IEEE Trans. Image Processing, vol. 12, no. 8, pp. 882-889, August 2003.
  10. A. Criminisi, P. Perez, and K. Toyama.(2004) “Region filling and object removal by exemplar-based inpainting” IEEE Trans. Image Process., vol.13, no.9, pp.12001212, 2004.
  11. S. Lankton and A. Tannenbaum. (2008) “Localizing region-based active contours”. IEEE Transactions on Image Processing, 17(11):20292039, 2008.
  12. R. Nikhil Pal and K. Sankar Pal. (1993) “A review on image segmentation techniques” Pattern Recognition, 26(9), pp 1277-1294.
  13. D. J. Heeger, and J. R. Bergen. (1995) “Pyramid-based texture analysis/synthesis” In Proceedings of ACM SIGGRAPH 95, ACM Press, 229238, 1995.
  14. R. Paget and D. Longstaff. (1995) “Texture synthesis via a nonparametric markov random field” In Proceedings of DICTA-95, Digital Image Computing: Techniques and Applications, volume 1, pp. 547552, 1995.
  15. H. Igehy, and L. Pereira. (1997) “Image replacement through texture synthesis” In IEEE International conference on Image Processing, vol. 3, pp. 186189. 1997.
  16. M. Bertalmio, G. Shapiro, V. Caselles and C. Ballester. (2000) “Image Inpainting” SIGGRAPH’00, pp.417424, 2000.
  17. J. Freixenet and X. Mu. (2002) “Yet another survey on image segmentation: Region and boundary information integration”. In 7th European Conference on Computer Vision (ECCV), pp 408-422.
  18. T. F. Chan, S. Kang, and J. Shen. (2002) “Euler’s elastica and curvature based inpaintings” SIAM J. Applied Mathematics, 63(2): pp. 564592, 2002.
  19. 1. K. Timothy Shih and Rong-Chi Chang. (2005) “Digital Inpainting Survey and Multilayer Image Inpainting Algorithms” Proceedings of the Third International Conference on Information Technology and Applications (ICITA’05) 0-7695-2316-1/05
  20. H. Cheng, W. Hsieh, K Lin, W. Wang and L Wu. (2005) “Robust Algorithm for Exemplar-based Image Inpainting” in Proceedings of International Conference on Computer Graphics, Imaging and Vision, pp. 64-69, Jul. 2005, Beijing, China.
  21. J. Sun, L. Yuan, J. Jia, and H.-Y. Shum (2005) “Image completion with structure propagation” In SIGGRAPH, 2005.
  22. X. Wang, D. Huang, H. Xu. (2010) “An efficient local Chan-Vese model for image segmentation”. Pattern Recognition 43: 603618. doi:10.1016/j.patcog.2009.08.002

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 733-739
Manuscript Number : CSEIT1725124
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Thi Thi Soe, Zarni Sann, "Occluded Object Reconstruction with Partial Appearance ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.733-739, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT1725124

Article Preview