Multiscale Bayesian segmentation using a trainable context model

IEEE Transactions on Image Processing

Volumn 10, Issue 4, 2001, Pages 511-525

  Cheng, Hui ^a,b   Bouman, Charles A ^a,c  

^a IEEE   (United States)

^b SARNOFF CORPORATION   (United States)

^c PURDUE UNIVERSITY   (United States)

Author keywords

Document segmentation; Image segmentation; Multiscale; Prior model; Training; Wavelet

Indexed keywords

TRAINABLE CONTEXT MODEL;

COMPUTATIONAL COMPLEXITY; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY; WAVELET TRANSFORMS;

IMAGE SEGMENTATION;

EID: 0035308978     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/83.913586     Document Type: Article

References (41)

1. Document analysis system
2. Document image understanding: Geometric and logical layout
3. A segmentation-based predictive multiresolution image coder
4. Bayes smoothing algorithms for segmentation of binary images modeled by Markov random fields
5. On the statistical analysis of dirty pictures
6. Modeling and segmentation of noisy and textured images using Gibbs random fields
7. Efficiency of pseudolikelihood estimation for simple Gaussian fields
8. Discrete-index Markov-type random processes
9. Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation
10. Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
11. ML parameter estimation for Markov random fields with applications to Bayesian tomography
12. Segmentation and estimation of image region properties through cooperative hierarchical computation
13. Supervised segmentation using a multiresolution data representation
14. Multiscale segmentation and anomaly enhancement of SAR imagery
15. Multiscale segmentation of unstructured document pages using soft decision integration
16. Multiresolution feature extraction and selection for texture segmentation
17. Texture classification and segmentation using wavelet frames
18. Texture segmentation using hierarchical wavelet decomposition
19. A renormalization group approach to image processing problems
20. Multiple resolution segmentation of textured images
21. Multiscale Markov random fields and constrained relaxation in low level image analysis
22. Multispectral image segmentation using a multiscale image model
23. A multiscale random field model for Bayesian image segmentation
24. Hierarchical statistical models for the fusion of multiresolution image date
25. Wavelet-based statistical signal processing using hidden Markov models
26. Parallel image classification using multiscale Markov random fields
27. Segmentation of textured images using a multiresolution Gaussian autoregressive model
28. An iterative growing and pruning algorithm for classification tree design
29. Multiscale document segmentation
30. Trainable context model for multiscale segmentation
31. Embedded image coding using zerotrees of wavelet coefficients
32. Multiscale autoregressive models and wavelets
33. Estimation and hypothesis testing in finite mixture models
34. Maximum likelihood from incomplete data via the EM algorithm
35. Parameter estimation of dependence tree models using the EM algorithm
36. Bayesian belief networks as a tool for stochatic parsing
37. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images
38. A universal prior for integers and estimation by minimum description length
39. Multiscale document compression algorithm