Edge-preserving filtering for grey and color image

2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

Volumn 5, Issue , 2009, Pages 139-143

  Huang, Xing Fang ^a,b   Zhang, Jiang She ^a,b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR IMAGES; EDGE PRESERVING; EXPLICIT FORMULATION; LOCAL-ADAPTIVE; NON-ITERATIVE; REAL IMAGES;

COMPUTER SCIENCE;

EID: 71049149296     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSIE.2009.631     Document Type: Conference Paper

References (21)

1. H. Barash, D. A fundamental relationship between bilateral filtering, adaptive smoothing and the nonlinear diffusion equation. IEEE Trans. on Pattern Analysis and Machine Intelligence 2002.24, 6.
2. P. Saint-Marc, J. Chen, and G. Medioni, "Adaptive Smoothing: A General Tool for Early Vision," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, pp. 514-529, 1991.
3. C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images; Computer Vision, 1998. Sixth International Conference on 4-7 Jan. 1998 Page(s):839-846.
4. P. Perona and J. Malik, "Scale Space and Edge Detection Using Anisotropic Diffusion," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 629-639, 1990.
5. F. Durand, and J. Dorsey, Fast bilateral filtering for the display of high-dynamic-range images. ACM Transactions on Graphics (TOG) 21, 3, 257-266. 2002.
6. M. Elad, on the origin of the bilateral filter and ways to improve it. IEEE Trans. on Image Processing, 2002, 11(10): 1141-1151.
7. R. Xu, S. N. Pattanaik. Non-Iterative, Robust Monte Carlo Noise Reduction, IEEE Computer Graphics and Applications. Volume 25(4), 2005, pages: 31-35.
8. K. Fukunaga, LD. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Info Theory 1975;21:32-40
9. BW. Silverman, Density Estimation for Statistics and Data Analysis, Chapman & Hall, 1987.
10. Y. Cheng Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Analysis and Machine Intelligence. 1995; 17: 790-799
11. D. Comaniciu and P. Meer, "Mean Shift: A Robust Approach toward Feature Space Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
12. D. Comaniciu, P. Meer: Robust Analysis of Feature Spaces: Color Image Segmentation, IEEE Conf. Computer Vision and Pattern Recognition (CVPR'97), San Juan, Puerto Rico, 750-755, 1997.
13. M. Fashing, C. Tomasi, Mean shift is a bound optimization. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2005, 27(3): 471-474.
14. Á. Miguel Carreira-Perpiñán, Gaussian mean shift is an EM algorithm". IEEE Trans. on Pattern Analysis and Machine Intelligence. (2007) 29(5):767-776.
15. D. Barash, D. Comaniciu: A Common Framework for Nonlinear Diffusion, Adaptive Smoothing, Bilateral Filtering and Mean Shift, Image and Video Computing, Vol. 22, No. 1, 73-81, 2004.
16. M. Stone, "Cross-validation and multinomial prediction." Biometrika, (1974). 64, 3, 509-515.
17. B. Efron, "Estimating the error rate of a prediction rule: Improvement on cross-validation," J. of the American Statistical Association, (1983). 78, 316-331.
18. W. Härdle, and J.S. Marron, "Optimal bandwidth selection in nonparametric regression function estimation." The Annals of Statistics. (1985) 13, 4, 1465-1481.
19. H. Takeda, S. Farsiu, P. Milanfar,; Kernel Regression for Image Processing and Reconstruction. IEEE Trans. on Image Processing, vol. 16, no. 2, pp. 349-366, Feb. 2007.
20. J. Zhang, X. Huang and C. Zhou, An improved kernel regression method based on Taylor expansion. Applied Mathematics and Computation, Volume 193, Issue 2, 1 November 2007, Pages 419-429
21. Gijbels, A. Lambert, P. Qiu, Edge-preserving image denoising and estimation of discontinuous surfaces, IEEE Trans. on Pattern Analysis and Machine Intelligence, Volume 28, Issue 7, July 2006 Page(s):1075-1087