Image Segmentation and Image Matting for Foreground Extraction using Active Contour Based Method
DOI:
https://doi.org/10.20894/IJMSR.117.003.001.003Keywords:
Image segmentation, active contour method, image matting, Gaussian filterAbstract
Image segmentation plays essential role in society for identification of objects, persons, and so on. The representation of the image is changed by the image segmentation method. It can change the boundaries and edges easily. It is used in the crime scene analysis for the identification of crime. An efficient method for image segmentation is proposed in this study. Active contour image segmentation method is used for segmentation in this study. The results are used in the code book algorithm. Experimental results show the performance of proposed method.
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References
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2. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transaction on System Management and Cybernetics, vol. SMC-9, no. 1, pp. 62–66, 1979.
3. N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition, vol. 26, no. 9, pp. 1277–1294, 1993.
4. P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y. C. Chen, “A survey of Thresholding techniques,” Computer Vision Graphics and Image Processing, vol. 41, pp. 233–260, 1988.
5. T. W. Ridler and S. Calvard, “Picture thresholding using an iterative selection Method,” IEEE Transaction on System Management and Cybernetics, vol. 8, pp. 630–632, 1978.
6. S.C. Zhu, X. Liu and Y. Wu, “Exploring texture ensembles by efficient Markov chain Monte Carlo”. IEEE Transaction On Pattern Analysis And Machine Intelligence, vol.22, no.6, pp.554–569, 2000.
7. L. Xiuwen and W. Deliang, “Texture Classification Using Spectral Histograms”. IEEE Transaction On Image Processing, vol.12, no.6, pp. 661-670, 2003.
8. G.L. Gimel’farb, “Texture modeling by multiple pairwise pixel interactions”. IEEE transaction On Pattern Analysis And machine Intelligence, vol.18, no.11,
pp.1110–1114, 1996.
9. J.F Aujol, G. Aubert and L. Blanc-F´eraud, “Wavelet-based level set evolution For classification of textured images”. IEEE Transaction On Image Processing, vol.12, no.12, pp.1634-1641, 2003.
10. A. Luminita, A. Vese and T.F. Chan, “A multiphase level set framework For image segmentation using the Mumford and Shah model”, International Journal Of Computer Vision, vol.50, no.3, pp.271-293, 2002.
11. C. Samson, L. Blanc-F´eraud, G. Aubert and J. Zerubia, “A level set method for image classification”. International Journal of Computer Vision, vol.40, no.3, pp.187-197, 2000.
12. Kaihua Zhang, Lei Zhang, Huihui Song, Wengang Zhou, “Active contours with selective local or global segmentation: A new formulation and level set method”, Elsevier Journal, Image and Vision Computing, Volume 28, pp. 668–676, 2010.
13. Gary R. M. And Linde Y., “Vector Quantizers and Predicative Quantizers for Gauss- Markov Sources,” IEEE Transactions on Communication, vol. 30, no. 2, pp 381-389, 1982.
14. Thrasyvoulos N. P., “An Adaptive Clustering Algorithm for Image Segmentation,” IEEE Transaction on Signal Processing, vol. 40, no. 4, pp. 901-914, 1992.
15. Tou J. T. And Gonzalez R. C., “Pattern Recognition Principles”, Addison Wesley, USA, pp. 75-97, 1974.
16. Yan M. X. H. And Karp J. S., “Segmentation of 3D Brain MR Using an Adaptive K- means Clustering Algorithm,” in Proceedings of the 4th IEEE Conference on Nuclear Science Symposium and Medical Imaging, San Francisco, USA, vol.4, pp. 1529-1533, 1995.
17. R. Urquhart., “Graph theoretical clustering based on limited neighborhood sets”, Pattern Recognition, vol 15:3, pages 173-187, 1982.
18. C.T. Zahn., “Graph-theoretic methods for detecting and describing gestalt clusters”,IEEE Transactions on Computing, vol 20, pages 68-86, 1971.
19. Vladimir Kolmogorov and Ramin Zabih., “What energy function can be minimized via graph cuts?” TPAMI, 26(2):147–159, February 2004.
20. A. X. Falaco, J.K. Udupa, S. Samarasekara, and S. Sharma., “User-steered image segmentation paradigms: Live wire and live lane.”, In Graphical Models and Image Processing, vol 60, pp 233–260, 1998.
21. C. Rosenberger,K. Chehdi, “Unsupervised Clustering Method with Optimal Estimation of the Number of Clusters: Application to Image Segmentation” in the proceedings of 15th International Conference on Pattern Recognition (ICPR'00) – vol 1, pp 1656.
22. R. Fielding., “The Technique of Special Effects Cinematography”, Focal/Hastings House, London, 3rd edition, 1972.
23. C.Rother, V. Kolmogorov, A. Blake, “Grabcut- Interactive foreground extraction using iterated graph cuts,” In Proceedings Of SIGGRAPH’2004, 2004.
24. J. Sun, J. Jia, C.-K. Tang, H.-Y. Shum “Poisson matting,” In Proceedings Of SIGGRAPH’2004, 2004.
25. M. Ruzon, C. Tomasi, “Alpha estimation in natural images,” In Proceedings Of International Conference on Computer Vision and Pattern Recognition 2000 (CVPR 2000), 2000.
26. Y.Y. Chuang, B. Curless, D.H. Salesin, R. Szeliski, “A Bayesian approach to digital matting,” In Proc. Of IEEE Internation Conference on Computer Vision 2001(ICCV 2001), 2001.
27. M. A. Ruzon and C. Tomasi., “Alpha estimation in natural images”, In CVPR 2000, pages 18-25, June 2000.
28. X. Li, Z. Zhao, and H. D. Cheng, “Fuzzy entropy threshold approach to breast cancer detection,” Information Science, vol. 4, pp. 49–56, 1995.
29. S. K. Pal, R. A. King, and A. A. Hashim, “Automatic gray level thresholding through index of fuzziness and entropy,” Pattern Recognition Letters, no. 1, pp. 141–146, 1983.
30. Orlando J. Tobias, and Rui Seara, “Image Segmentation by Histogram Thresholding Using Fuzzy Sets”, IEEE Transactions On Image Processing, vol. 11, No. 12, December 2002, 1457-1465.
31. T. Randen and J.H. Husoy, “Filtering for Texture classification: A Comparative Study”. IEEE Transaction On Pattern Analysis And Machine Intelligence, vol. 21, no.4, pp.291-310, 1999.
32. I. Karoui , R. Fablet , J.M. Boucher , J.M. Augustin, “Region-Based Image Segmentation Using Texture Statistics And Level-Set Methods”.
33. Vincent L. And Soille P. “Watershed in Digital Space: An Efficient Algorithm Based on Immersion Simulations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-593, 1991.
34. Nassir Salman, “Image Segmentation Based on Watershed and Edge Detection Techniques”, The International Arab Journal of Information Technology, Vol. 3, No. 2, April 2006.
35. Pedro F. Felzenszwalb, Daniel P. Huttenlocher, “Efficient Graph-Based Image Segmentation” pp 1-26.
36. Yuri Boykov and M. P. Jolly., “Interactive graph cuts for optimal boundary and region segmentation”, In ICCV, volume I, pages 105–112, 2001.
37. Bo Peng, Olga Veksler, “Parameter Selection for Graph Cut Based Image Segmentation”, NSERC, Canada. Pp 1-10.
38. Rezaee, M.R. van der Zwet, P.M.J. Lelieveldt, B.P.E. van der Geest, R.J. Reiber, J.H.C., 2000,“A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering” in IEEE Transactions on Image Processing, pp: 1238-1248, Vol. 9, No: 7, Jul 2000
39. Yung-Yu Chuang, Brian Curless, David H. Salesin, Richard Szeliski, “A Bayesian Approach to Digital Matting”, IEEE Transactions on Image Processing, pp:264- 271.
40. Jian Sun, Jiaya Jia, Chi-Keung Tang, Heung-Yeung Shum, “Poisson Matting”, ACM SIGGRAPH conference proceedings, pp 1-7.
41. T. Chan, L. Vese., “Active contours without edges”, IEEE Transaction on Image Processing 10 (2) (2001) 266–277.
42. D. Mumford, J. Shah., “Optimal approximation by piecewise smooth function And associated variational problems”, Communication on Pure and Applied Mathematics 42 (1989) 577–685.
43. C.Y. Xu, A. Yezzi Jr., J.L. Prince., “On the relationship between parametric and Geometric active contours”, in Processing of 34th Asilomar Conference on Signals Systems and Computers, 2000, pp. 483–489.
44. C. Davatzikos, J.L. Prince, “An active contour model for mapping the cortex”, IEEE Transaction on Medical Imaging 14 (1995) 65–80.
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2011-12-20
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