A multiscale image segmentation method

Pattern Recognition

Volumn 52, Issue , 2016, Pages 332-345

  Li, Yafeng ^a   Feng, Xiangchu ^b  

^a BAOJI UNIVERSITY OF ARTS AND SCIENCES   (China)

^b XIDIAN UNIVERSITY   (China)

Author keywords

Image segmentation; Multiscale decomposition; Total variation; Variational model

Indexed keywords

FEATURE EXTRACTION; NUMERICAL METHODS;

IMAGE SEGMENTATION AND FEATURE EXTRACTIONS; MULTI-SCALE DECOMPOSITION; MULTI-SCALE IMAGE SEGMENTATION; PARAMETERS SELECTION; REGION OF INTEREST; REGIONS OF INTEREST; TOTAL VARIATION; VARIATIONAL MODELING;

IMAGE SEGMENTATION;

EID: 84951787250     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2015.10.004     Document Type: Article

References (66)

1. I. Kokkinos, and P. Maragos Synergy between object recognition and image segmentation using the expectation-maximization algorithm IEEE Trans. Pattern Anal. Mach. Intell. 31 8 2009 1486 1501
2. Y. Kang, K. Yamaguchi, T. Naito, and Y. Ninomiya Multiband image segmentation and object recognition for understanding road scenes IEEE Trans. Intell. Transp. Syst. 12 4 2011 1423 1433
3. Y. Han, D. Zheng, G. Baciu, X. Feng, and M. Li Fuzzy region competition-based auto-color-theme design for textile images Text. Res. J. 83 6 2013 638 650
4. J. Zhang, R. Lai, and C.-C. Kuo Adaptive directional total-variation model for latent fingerprint segmentation IEEE Trans. Inf. Forensics Secur. 8 8 2013 1261 1273
5. A. Ahmadvand, and M.R. Daliri MR brain image segmentation methods and applications OMICS J. Radiol. 3 2014 2167 7964
6. T. Chan, and L. Vese Active contours without edges IEEE Trans. Image Process. 10 2 2001 266 277
7. L. Vese, and T. Chan A multiphase level set framework for image segmentation using the mumford and shah model Int. J. Comput. Vis. 50 3 2002 271 293
8. C. Li, R. Huang, Z. Ding, J. Gatenby, D. Metaxas, and J. Gore A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri IEEE Trans. Image Process. 20 7 2011 2007 2016
9. S. Liu, and Y. Peng A local region-based chan-vese model for image segmentation Pattern Recognit. 45 7 2012 2769 2779
10. B. Mory, R. Ardon, and J.-P. Thiran Variational segmentation using fuzzy region competition and local non-parametric probability density functions ICCV IEEE 2007 1 8
11. F. Li, M.K. Ng, T. Zeng, and C. Shen A multiphase image segmentation method based on fuzzy region competition SIAM J. Imaging Sci. 3 3 2010 277 299
12. F. Li, and M.K. Ng Kernel density estimation based multiphase fuzzy region competition method for texture image segmentation Commun. Comput. Phys. 8 3 2010 623 641
13. S.K. Choy, M.L. Tang, and C.S. Tong Image segmentation using fuzzy region competition and spatial/frequency information IEEE Trans. Image Process. 20 6 2011 1473 1484
14. J. Liu, X. Tai, H. Huang, and Z. Huan A fast segmentation method based on constraint optimization and its applications: intensity inhomogeneity and texture segmentation Pattern Recognit. 44 9 2011 2093 2108
15. Y. Han, W.W. Wang, and X.C. Feng A new fast multiphase image segmentation algorithm based on nonconvex regularizer Pattern Recognit. 45 1 2012 363 372
16. H. Zhang, X. Ye, and Y. Chen An efficient algorithm for multiphase image segmentation with intensity bias correction IEEE Trans. Image Process. 22 10 2013 3842 3851
17. D. Mumford, and J. Shah Optimal approximation by piecewise smooth function and associated variational problems Commun. Pure Appl. Math. 42 1989 577 685
18. Z. Tu, and S. Zhu Image segmentation by data-driven Markov chain Monte Carlo IEEE Trans. Pattern Anal. Mach. Intell. 24 5 2002 657 673
19. A.C. Fan, J.W. Fisher, W.M. Wells, J.J. Levitt, and A.S. Willsky MCMC curve sampling for image segmentation Medical Image Computing and Computer-Assisted Intervention 2007 Springer-Verlag Berlin Heidelberg 477 485
20. Z. Kato Segmentation of color images via reversible jump MCMC sampling Image Vis. Comput. 26 3 2008 361 371
21. L. Ding, A. Yilmaz, and R. Yan Interactive image segmentation using Dirichlet process multiple-view learning IEEE Trans. Image Process. 21 4 2012 2119 2129
22. J. Shi, and J. Malik Normalized cuts and image segmentation IEEE Trans. Pattern Anal. Machch. Intell. 22 8 2000 888 905
23. S. Chen, L. Cao, Y. Wang, J. Liu, and X. Tang Image segmentation by MAP-ML estimations IEEE Trans. Image Process. 19 9 2010 2254 2264
24. B. Peng, L. Zhang, D. Zhang, and J. Yang Image segmentation by iterated region merging with localized graph cuts Pattern Recognit. 44 10-11 2011 2527 2538
25. M. Jung, M. Kang, and M. Kang Variational image segmentation models involving non-smooth data-fidelity terms J. Sci. Comput. 59 2 2014 277 308
26. Y. Meyer Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, University Lecture Series 2001 American Mathematical Society Boston
27. L. Rudin, S. Osher, and E. Fatemi Nonlinear total variation based noise removal algorithms Physica D 60 1992 259 268
28. L. Vese, and S. Osher Modeling textures with total variation minimization and oscillating patterns in image processing J. Sci. Comput. 19 1 2003 553 572
29. S. Osher, A. Solè, and L. Vese Image decomposition and restoration using total variation minimization and the H-1 norm Multisc. Model. Simul. 1 3 2003 349 370
30. J.-F. Aujol, G. Aubert, L. Blanc-Fèrau, and A. Chambolle Image decomposition into a bounded variation component and an oscillating component J. Math. Imaging Vis. 22 1 2005 71 88
31. J.-F. Aujol, and A. Chambolle Dual norms and image decomposition models Int. J. Comput. Vis. 63 1 2005 85 104
32. A. Buades, T.M. Le, J.-M. Morel, and L.A. Vese Fast cartoon+texture image filters IEEE Trans. Image Process. 19 8 2010 1978 1986
33. M. Bertalmio, L. Vese, G. Sapiro, and S. Osher Simultaneous structure and texture image inpainting IEEE Trans. Image Process. 12 8 2003 882 889
34. W.T. Yin, D. Goldfarb, S. Osher, Image cartoon-texture decomposition and feature selection using the total variation regularized L1 functional, in: Proceedings of the Third International Conference on Variational, Geometric, and Level Set Methods in Computer Vision, Springer-Verlag Berlin, Heidelberg, 2005, pp. 73-84.
35. W.T. Yin, D. Goldfarb, and S. Osher The total variation regularized L1 model for multiscale decomposition SIAM J. Multisc. Model. Simul. 6 1 2007 190 211
36. W.T. Yin, D. Goldfarb, and S. Osher A comparison of three total variation based texture extraction models J. Vis. Commun. Image Represent. 18 3 2007 240 252
37. V. Duval, J.-F. Aujol, and Y. Gousseau The TVL1 model: a geometric point of view SIAM J. Multisc. Model. Simul. 8 1 2009 154 189
38. T. Chan, and S. Esedoglu Aspects of total variation regularized l1 function approximation SIAM J. Appl. Math. 65 5 2005 1817 1837
39. T. Chen, W.T. Yin, X.S. Zhou, D. Comaniciu, and T.S. Huang Total variation models for variable lighting face recognition IEEE Trans. Pattern Anal. Mach. Intell. 28 9 2006 1519 1524
40. J.-L. Starck, M. Elad, and D.L. Donoho Image decomposition via the combination of sparse representations and a variational approach IEEE Trans. Image Process. 14 10 2005 1570 1582
41. L.L. Jiang, X.C. Feng, and H.Q. Yin Variational image restoration and decomposition with curvelet shrinkage J. Math. Imaging Vis. 30 2 2008 125 132
42. M.J. Fadili, J.-L. Starck, J. Bobin, and Y. Moudden Image decomposition and separation using sparse representations: an overview Proc. IEEE 2010 983 994
43. Y.F. Li, and X.C. Feng Image decomposition via learning the morphological diversity Pattern Recognit. Lett. 33 2 2012 111 120
44. H. Schaeffer, and S. Osher A low patch-rank interpretation of texture SIAM J. Imaging Sci. 6 1 2013 226 262
45. S. Ono, T. Miyata, and I. Yamada Cartoon-texture image decomposition using blockwise low-rank texture characterization IEEE Trans. Image Process. 23 3 2014 1128 1142
46. L.M. Briceno-Arias, P.L. Combettes, J.-C. Pesquet, and N. Pustelnik Proximal algorithms for multicomponent image recovery problems J. Math. Imaging Vis. 41 1 2011 3 22
47. S. Ono, T. Miyata, I. Yamada, and K. Yamaoka Image recovery by decomposition with component-wise regularization IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 95 12 2012 2470 2478
48. M.K. Ng, X. Yuan, and W. Zhang Coupled variational image decomposition and restoration model for blurred cartoon-plus-texture images with missing pixels IEEE Trans. Image Process. 22 6 2013 2233 2246
49. M. Elad, J.-L. Starck, P. Querre, and D.L. Donoho Simultaneous cartoon and texture image in painting using morphological component analysis Appl. Comput. Harmonic Anal. 19 3 2005 340 358
50. J.-F. Cai, R.H. Chan, and Z. Shen Simultaneous cartoon and texture image inpainting Inverse Probl. Imaging 4 3 2010 379 395
51. D. Paquin, D. Levy, E. Schreibmann, and L. Xing Multiscale image registration Math. Biosci. Eng. 3 2006 389 418
52. J. Bobin, J.-L. Starck, M.J. Fadili, and Y. Moudden Sparsity and morphological diversity in blind source separation IEEE Trans. Image Process. 16 11 2007 2662 2674
53. R.B. Potts, and C. Domb Some generalized order-disorder transformations Math. Proc. Camb. Philos. Soc. 1952 106 109
54. C. Nieuwenhuis, E. Töppe, and D. Cremers A survey and comparison of discrete and continuous multi-label optimization approaches for the Potts model Int. J. Comput. Vis. 104 3 2013 223 240
55. C. Zach, D. Gallup, J.-M. Frahm, M. Niethammer, in: O. Deussen, D.A. Keim, D. Saupe (Eds.), Fast Global Labeling for Real-Time Stereo using Multiple Plane Sweeps, VMV, Aka GmbH, 2008, pp. 243-252.
56. C. Tai, X.Q. Zhang, and Z.W. Shen Wavelet frame based multi-phase image segmentation SIAM J. Imaging Sci. 6 4 2013 2521 2546
57. T. Chen, W.T. Yin, X.S. Zhou, D. Comaniciu, T.S. Huang, Illumination normalization for face recognition and uneven background correction using total variation based image models, in: CVPR, IEEE Computer Society, 2005, pp. 532-539.
58. W.T. Yin, T. Chen, X.S. Zhou, and A. Chakraborty Background correction for cDNA microarray image using the TV+L1 model Bioinformatics 21 10 2005 2410 2416
59. C. Zach, T. Pock, H. Bischof, A duality based approach for realtime TV-l1 optical flow, in: Proceedings of the 29th DAGM conference on pattern recognition, Springer-Verlag, Berlin Heidelberg, 2007, pp. 214-223.
60. M. Nikolova A variational approach to remove outliers and impulse noise J. Math. Imaging Vis. 20 1-2 2004 99 120
61. T. Goldstein, and S. Osher The split Bregman method for L1-regularized problems SIAM J. Imaging Sci. 2 2 2009 323 343
62. S. Setzer Split Bregman algorithm, Douglas-Rachford splitting and frame shrinkage X.-C. Tai, K. Mørken, M. Lysaker, K.-A. Lie, SSVM, Lecture Notes in Computer Science, Springer vol. 5567 2009 Springer 464 476
63. A. Chambolle An algorithm for total variation minimization and applications J. Math. Imaging Vis. 20 1-2 2004 89 97
64. J.M. Fadili, and G. Peyre Total variation projection with first order schemes IEEE Trans. Image Process. 20 3 2011 657 669
65. A.K. Jain, and F. Farrokhnia Unsupervised texture segmentation using Gabor filters Pattern Recognit. 24 12 1991 1167 1186
66. B.S. He, H. Yang, and S.L. Wang Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities J. Optim. Theory Appl. 106 2 2000 337 356