8.8.9 Noise Models in Segmentation

Chapter Contents (Back)
Noise. Texture Segmentation.

Bell, Z.,
A Bayesian/Monte Carlo Segmentation Method for Images Dominated by Gaussian Noise,
PAMI(11), No. 9, September 1989, pp. 985-990.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 8909

Goutsias, J., and Mendel, J.M.,
Simultaneous Optimal Segmentation and Model Estimation of Nonstationary Noisy Images,
PAMI(11), No. 9, September 1989, pp. 990-998.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 8909

Wang, T.[Tao], Zhuang, X.H.[Xin-Hua], Xing, X.L.[Xiao-Liang],
Robust and Adaptive Segmentation of Noisy Images Using Gibbs Random Field Models,
PRAI(6), 1992, pp. 753-775. BibRef 9200
And:
The use of Gibbs random fields for image segmentation,
ICPR92(III:57-60).
IEEE DOI Link 9208
BibRef

Wang, T., Zhuang, X., Xing, X.,
Robust Segmentation of Noisy Images Using a Neural Network Model,
IVC(10), No. 4, May 1992, pp. 233-240.
WWW Version. BibRef 9205

Spann, M., Grace, A.E.,
Adaptive Segmentation of Noisy and Textured Images,
PR(27), No. 12, December 1994, pp. 1717-1733.
WWW Version. BibRef 9412

Murino, V., Ottonello, C., Pagnan, S.,
Noisy Texture Classification: A Higher Order Statistics Approach,
PR(31), No. 4, April 1998, pp. 383-393.
WWW Version. 9803
BibRef


Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Textures and Color for Segmentation .


Last update:Nov 16, 2009 at 19:35:14