article

Parameter estimation in hidden fuzzy Markov random fields and image segmentation

Authors:

Fabien Salzenstein,

Wojciech PieczynskiAuthors Info & Claims

Graphical Models and Image Processing, Volume 59, Issue 4

Pages 205 - 220

https://doi.org/10.1006/gmip.1997.0431

Published: 01 July 1997 Publication History

Abstract

No abstract available.

Cited By

View all

Li LJiang Y(2023)A Modified Fuzzy Markov Random Field Incorporating Multiple Features for Liver Tumor SegmentationArtificial Intelligence10.1007/978-981-99-8850-1_29(352-363)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-8850-1_29
Filali HKalti K(2021)Image segmentation using MRF model optimized by a hybrid ACO-ICM algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-05957-125:15(10181-10204)Online publication date: 30-Jun-2021
https://dl.acm.org/doi/10.1007/s00500-021-05957-1
Yang RQian XZhang B(2019)Multi-Feature Fusion Aerial Image Segmentation in Complex BackgroundProceedings of the 3rd International Conference on Vision, Image and Signal Processing10.1145/3387168.3387237(1-8)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3387168.3387237
Show More Cited By

Index Terms

Parameter estimation in hidden fuzzy Markov random fields and image segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
    2. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
2. Theory of computation
  1. Design and analysis of algorithms
  2. Models of computation
    1. Probabilistic computation

Recommendations

Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation

This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the ...
Markov Random Fields in Image Segmentation
Pixon-based image segmentation with Markov random fields

Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Graphical Models and Image Processing

Graphical Models and Image Processing Volume 59, Issue 4

July 1997

70 pages

ISSN:1077-3169

Editors:
Norman I. Badler
Univ. of Pennsylvania, Philadelphia
,
Ingrid Carlbom
Bell Labs, Murry Hill, NJ

Issue’s Table of Contents

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 July 1997

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Li LJiang Y(2023)A Modified Fuzzy Markov Random Field Incorporating Multiple Features for Liver Tumor SegmentationArtificial Intelligence10.1007/978-981-99-8850-1_29(352-363)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-8850-1_29
Filali HKalti K(2021)Image segmentation using MRF model optimized by a hybrid ACO-ICM algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-05957-125:15(10181-10204)Online publication date: 30-Jun-2021
https://dl.acm.org/doi/10.1007/s00500-021-05957-1
Yang RQian XZhang B(2019)Multi-Feature Fusion Aerial Image Segmentation in Complex BackgroundProceedings of the 3rd International Conference on Vision, Image and Signal Processing10.1145/3387168.3387237(1-8)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3387168.3387237
Touati RMignotte MDahmane M(2019)Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise-Based Markov Random Field ModelIEEE Transactions on Image Processing10.1109/TIP.2019.293374729(757-767)Online publication date: 9-Oct-2019
https://dl.acm.org/doi/10.1109/TIP.2019.2933747
Zhang PLi MWu YAn LJia L(2016)Unsupervised SAR image segmentation using high-order conditional random fields model based on product-of-expertsPattern Recognition Letters10.1016/j.patrec.2016.03.03278:C(48-55)Online publication date: 15-Jul-2016
https://dl.acm.org/doi/10.1016/j.patrec.2016.03.032
Jain NKumar V(2016)IFCM Based Segmentation Method for Liver Ultrasound ImagesJournal of Medical Systems10.1007/s10916-016-0623-140:11(1-12)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s10916-016-0623-1
Peng RVarshney P(2015)On performance limits of image segmentation algorithmsComputer Vision and Image Understanding10.1016/j.cviu.2014.11.004132:C(24-38)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.cviu.2014.11.004
Crivelli TBouthemy PCernuschi-Frías BYao J(2011)Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random FieldInternational Journal of Computer Vision10.1007/s11263-011-0429-z94:3(295-316)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1007/s11263-011-0429-z
Salzenstein FCollet C(2006)Fuzzy Markov Random Fields versus Chains for Multispectral Image SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2006.22828:11(1753-1767)Online publication date: 1-Nov-2006
https://dl.acm.org/doi/10.1109/TPAMI.2006.228
Destrempes FMignotte M(2004)A Statistical Model for Contours in ImagesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2004.127394026:5(626-638)Online publication date: 1-May-2004
https://dl.acm.org/doi/10.1109/TPAMI.2004.1273940
Show More Cited By

Abstract

Cited By

Index Terms

Recommendations

Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation

Markov Random Fields in Image Segmentation

Pixon-based image segmentation with Markov random fields

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations