Sea ice segmentation using Markov random fields

被引：0

作者：

Yue, B ^{[1
]}

Clausi, DA ^{[1
]}

机构：

[1] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada

来源：

IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS | 2001年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a mean of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework.

引用

页码：1877 / 1879

页数：3

共 50 条

[31] Multiscale Bayesian texture segmentation using neural networks and Markov random fields
Kim, Tae Hyung
Eom, Il Kyu
Kim, Yoo Shin
[J]. NEURAL COMPUTING & APPLICATIONS, 2009, 18 (02): : 141 - 155
[32] UNSUPERVISED SEGMENTATION OF NOISY AND TEXTURED IMAGES USING MARKOV RANDOM-FIELDS
WON, CS
DERIN, H
[J]. CVGIP-GRAPHICAL MODELS AND IMAGE PROCESSING, 1992, 54 (04): : 308 - 328
[33] Texture segmentation of images on the basis of Markov random fields
Kovtun, I.V.
[J]. Upravlyayushchie Sistemy i Mashiny, 2003, (04): : 46 - 56
[34] Double Markov random fields and Bayesian image segmentation
Melas, DE
Wilson, SP
[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2002, 50 (02) : 357 - 365
[35] LOCALLY WEIGHTED MARKOV RANDOM FIELDS FOR CORTICAL SEGMENTATION
Cardoso, Manuel Jorge
Clarkson, Matthew J.
Modat, Marc
Ridgway, Gerard R.
Ourselin, Sebastien
[J]. 2010 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, 2010, : 956 - 959
[36] Extended Markov random fields for predictive image segmentation
Stolkin, R.
Hodgetts, M.
Greig, A.
Gilby, J.
[J]. PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON ADVANCES IN PATTERN RECOGNITION, 2007, : 208 - +
[37] Pairwise and Hidden Markov Random Fields in Image Segmentation
Courbot, Jean-Baptiste
Mazet, Vincent
[J]. 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), 2021, : 2458 - 2462
[38] Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields
Subbanna, Nagesh K.
Rajashekar, Deepthi
Cheng, Bastian
Thomalle, Goetz
Fiehler, Jens
Arbel, Tal
Forkert, Nils D.
[J]. FRONTIERS IN NEUROLOGY, 2019, 10
[39] Mammographic image segmentation using a tissue-mixture model and Markov random fields
McGarry, G
Deriche, M
[J]. 2000 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL III, PROCEEDINGS, 2000, : 416 - 419
[40] A segmentation method using compound Markov random fields based on a general boundary model
Wu, J
Chung, ACS
[J]. 2005 International Conference on Image Processing (ICIP), Vols 1-5, 2005, : 1521 - 1524

← 1 2 3 4 5 →