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Elimination of Glass Artifacts and Object Segmentation

by Vini Katyal, Aviral, Deepesh Srivastava
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 19
Year of Publication: 2012
Authors: Vini Katyal, Aviral, Deepesh Srivastava
10.5120/6215-8919

Vini Katyal, Aviral, Deepesh Srivastava . Elimination of Glass Artifacts and Object Segmentation. International Journal of Computer Applications. 43, 19 ( April 2012), 48-52. DOI=10.5120/6215-8919

@article{ 10.5120/6215-8919,
author = { Vini Katyal, Aviral, Deepesh Srivastava },
title = { Elimination of Glass Artifacts and Object Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 19 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 48-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number19/6215-8919/ },
doi = { 10.5120/6215-8919 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:16.405417+05:30
%A Vini Katyal
%A Aviral
%A Deepesh Srivastava
%T Elimination of Glass Artifacts and Object Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 19
%P 48-52
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Many images nowadays are captured from behind the glasses and may have certain stains discrepancy because of glass and must be processed to make differentiation between the glass and objects behind it. This research paper proposes an algorithm to remove the damaged or corrupted part of the image and make it consistent with other part of the image and to segment objects behind the glass. The damaged part is removed using total variation inpainting method and segmentation is done using kmeans clustering, anisotropic diffusion and watershed transformation. The final output is obtained by interpolation. This algorithm can be useful to applications in which some part of the images are corrupted due to data transmission or needs to segment objects from an image for further processing.

References
  1. Sonka. M. , Hilavac. V, Boyle, R. Image Processing, Analysis, and Machine Vision, 1998, 2nd edn. Brooks/Cole.
  2. Wen Guo, Changshen Xu, Songde Ma, Min Xu," Visual Attention Based Small Object Segmentation In Natural Images" Proceedings of 2010 IEEE 17th International Conference on Image Processing.
  3. Shen, Tingzhi; Wang, Lei," Adaptive approach to small object segmentation", Proc. SPIE 4550, 394(2001)
  4. Rudolf M. Bolle; Jonathan Connell," System And Method For Segmentation Of Images Of Objects That Are Occluded By A Semi-transparent Material - Patent 6668078", Issue Date: 12/23/2003.
  5. Hany Farid, Edward H. Adelson," Separating Reflections and Lighting Using Independent Components Analysis", In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1:262-267, 1999, Fort Collins CO.
  6. Tony F. Chan, Andy M. Yip Frederick E. Park," Simultaneous Total Variation Image In-painting and Blind deconvolution" Department of Mathematics, University of California, 405 Hilgard Avenue, Los Angeles, July 7, 2004
  7. Pietro perona and Jitendra malik, "Scale-Space and Edge Detection Using Anisotropic Diffusion", IEEE Transactions on pattern analysis and machine intelligence, Vol. 12. No. 7. July 1990
  8. J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1:281-297
  9. Beucher S. , Meyer F. : The morphological approach to segmentation: The watershed transformation. Mathematical Morphology in Image Processing (E. R. Dougherty, Ed. ), Dekker, New York, 1993, pp. 433-482
  10. Fernand Meyer," The watershed concept and its use in segmentation: a brief history" Centre de Morphologie Mathematique Department Maths et Systernes Mines-ParisTech, February 2, 2012
  11. MATLAB, User's Guide Version 7. 8. The MathWorks. Inc.
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation In-painting K-means Clustering Watershed Transform Anisotropic Diffusion

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