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 DOI Link 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 DOI Link 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:Feb 8, 2012 at 11:25:05