Name
Title | ||
|---|---|---|
Bayes smoothing algorithms for segmentation of images modeled by Markov random fields |
Abstract | ||
|---|---|---|
A new image segmentation algorithm is presented, based on recursive Bayes smoothing of images modeled by Markov random fields and corrupted by independent additive noise. The Bayes smoothing algorithm presented is an extension of a 1-D algorithm to 2-D and it yields the a posteriori distribution and the optimum Bayes estimate of the scene value at each pixel, using the total noisy image data. Computational concerns in 2-D, however, necessitate certain simplifying assumptions on the model and approximations on the implementation of the algorithm. In particular, the scene (noiseless image) is modeled as a Markov mesh random field and the algorithm is applied on (horizontal/vertical) strips of the image. The Bayes smoothing algorithm is applied to segmentation of two level test images and remotely sensed SAR data obtained from SEASAT, yielding remarkably good segmentation results even for very low signal to noise ratios. |
Year | DOI | Venue |
|---|---|---|
1984 | 10.1109/ICASSP.1984.1172642 | Acoustics, Speech, and Signal Processing, IEEE International Conference ICASSP '84. |
Keywords | Field | DocType |
strips,pixel,algorithms,bayes estimator,layout,signal to noise ratio,image processing,random field,two dimensional,computations,markov processes,testing,mathematical models,remote sensing,bayes theorem,image segmentation | Markov process,Random field,Scale-space segmentation,Pattern recognition,Computer science,Markov chain,Image processing,Algorithm,Image segmentation,Smoothing,Artificial intelligence,Bayes' theorem | Conference |
Volume | Citations | PageRank |
9 | 26 | 11.88 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haluk Derin | 1 | 162 | 151.29 |
| Elliott, Howard | 2 | 483 | 212.03 |
| Roberto Cristi | 3 | 158 | 203.50 |
| Donald Geman | 4 | 1868 | 495.62 |