Color texture modeling and color image decomposition in a variational-PDE approach

Proceedings of the 8th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2006

Volumn , Issue , 2006, Pages 103-110

  Vese, Luminita A ^a   Osher, Stanley J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR; IMAGE DENOISING; IMAGE SEGMENTATION; IMAGE TEXTURE; TEXTURES; VECTOR SPACES; VECTORS;

COLOR IMAGE DE-NOISING; COLOR IMAGE DECOMPOSITIONS; COLOR TEXTURES; OSCILLATORY FUNCTIONS; RGB SPACE; VARIATIONAL MODELING; VECTOR-VALUED FUNCTION;

COLOR IMAGE PROCESSING;

EID: 46449097530     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SYNASC.2006.24     Document Type: Conference Paper

References (38)

1. L. Alvarez, F. Guichard, P.L. Lions, J.M. Morel. Axioms and fundamental equations of image-processing. Archive for Rat. Mech. and An., 123(3): 199-257, 1993.
2. G. Aubert, L. Vese. A variational method in image recovery. SIAM J. on Num. Anal., 34(5): 1948-1979, 1997.
3. J.F. Aujol, S.H.Kang. Color image decomposition and restoration. JVCIR, 17(4): 916-928, 2006.
4. C. Ballester, M. Gonzalez. Affine invariant texture segmentation and shape from texture by variational methods. JMIV, 9(2): 141-171, 1998.
5. P. Blomgren, T.F. Chan. Color TV: Total variation methods for restoration of vector-valued images. IEEE TIP, 7(3): 304-309, 1998.
6. E.J. Candès, F. Guo. New Multiscale Transforms, Minimum Total Variation Synthesis: Applications to Edge-Preserving Image Reconstruction. Signal Processing, 82: 1519-1543, 2002.
7. S. Casadei, S. Mitter, P. Perona. Boundary detection in piecewise homogeneous textured images. LNCS, 588: 174-183, 1992.
8. A. Chambolle, P.L. Lions. Image recovery via total variation minimization and related problems. Numer. Math., 76: 167-188, 1997.
9. T.F. Chan, S.H. Kang. Total variation denoising and enhancement of color images based on the CB and HSV color models. JVCIR, 12(4): 422-435, 2001.
10. T.F. Chan, B.Y. Sandberg, L.A. Vese. Active contours without edges for vector-valued images. JVCIR, 11(2): 130-141, 2000.
11. T. Chan, J.H. Shen. Variational restoration of nonflat image features: Models and algorithms. SIAM J. on Applied Maths., 61(4): 1338-1361 2000.
12. T. Chan, L. Vese. An active contour model without edges. LNCS, 1682: 141-151, 1999.
13. T.F. Chan, L.A. Vese. Active contours without edges. IEEE TIP, 10(2): 266-277, 2001.
14. S. Geman, D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE TPAMI, 6(6): 721-741, 1984.
15. R. Kimmel, R. Malladi, N. Sochen. Images as embedded maps and minimal surfaces: Movies, color, texture, and volumetric medical images. IJCV, 39(2): 111-129, 2000.
16. R. Kimmel, N. Sochen. Orientation diffusion or how to comb a porcupine. JVCIR, 13(1-2): 238-248, 2002.
17. G. Koepfler, C. Lopez, J.M. Morel. A multiscale algorithm for image segmentation by variational method. SIAMJ. Num. Anal., 31(1): 282-299, 1994.
18. T.S. Lee. A Bayesian framework for understanding texture segmentation in the primary visual-cortex. Vision Research, 35(18): 2643-2657, 1995.
19. T.S. Lee, D. Mumford, A. Yuille. Texture segmentation by minimizing vector-valued energy functionals - the coupled-membrane model. LNCS, 588: 165-173, 1992.
20. F. Malgouyres. Mathematical analysis of a model which combines total variation and wavelet for image restoration. Journal of information processes, 2(1): 1-10, 2002.
21. F. Malgouyres. Combining total variation and wavelet packet approaches for image de-blurring. Proceedings, IEEE Workshop on VLSM in Computer Vision, 57-64, 2001.
22. F. Malgouyres. Minimizing the total variation under a general convex constraint for image restoration. IEEE TIP, 11(12): 1450-1456, 2002.
23. D. Mumford, J. Shah. Optimal approximations by piecewise smooth functions and associated variational-problems. Communications on Pure and Appl. Maths., 42(5): 577-685, 1989.
24. Y. Meyer. Oscillating Patterns in Image Processing and Nonlinear Evolution Equations. AMS 22, 2002.
25. N. Paragios, R. Deriche. Geodesic active regions and level set methods for supervised texture segmentation. IJCV, 46(3): 223-247, 2002.
26. P. Perona, J. Malik. Scale-space and edge-detection using anisotropic diffusion. IEEE TPAMI, 12(7): 629-639, 1990.
27. L. Rudin, S. Osher. Total variation based image restoration with free local constraints. Proc. IEEE ICIP Austin, Texas, I 31-35, 1994.
28. L. Rudin, S. Osher, E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60: 259-268, 1992.
29. B. Sandberg, T. Chan, L. Vese. A Level-Set and Gabor-Based Active Contour Algorithm for Segmenting Textured Images. UCLA CAM Report 02-39, 2002.
30. G. Sapiro. Color Snakes. CVIU, 68(2): 247-253, 1997.
31. G. Sapiro, D. Ringach. Anisotropic diffusion of multi-valued images with applications to color filtering. IEEE TIP, 5(11): 1582-1586, 1996.
32. B. Tang, G. Sapiro, V. Caselles. Color image enhancement via chromaticity diffusion. IEEE TIP, 10(5): 701-707, 2001.
33. P.E. Trahanias, D. Karakos, A.N. Venetsanopoulos. Directional processing of color images: Theory and experimental results. IEEE TIP, 5(6): 868-880, 1996.
34. L.A. Vese, S.J. Osher. Numerical methods for p-harmonic flows and applications to image processing. SIAM J. of Num. Anal., 40(6): 2085-2104, 2003.
35. L.A. Vese, S.J. Osher. Modeling textures with total variation minimization and oscillating patterns in image processing. JSC, 19(1-3): 553-572, 2003.
36. L.A. Vese and S.J. Osher. Image denoising and decomposition with total variation minimization and oscillatory functions. JMIV, 20(1-2): 7-18, 2004.
37. S.C. Zhu, Y.N. Wu, D. Mumford. Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling. IJCV, 27(2): 107-126, 1998.
38. S.C. Zhu, Y.N. Wu, D. Mumford. Minimax entropy principle and its application to texture modeling. Neural Computation, 9(8): 1627-1660, 1997.