Conditional Random Field Segmentation
Conditional Random Field Segmentation is a probabilistic graphical model-based image segmentation method that employs conditional random fields to model the dependencies between pixel labels and observed image features, enabling structured prediction by integrating local evidence with global contextual coherence.
277
Publications
17.2K
Citations
1K
Authors
407
Institutions
Publications per year
2017–2026
149
Parents
277
Learning transportation mode from raw gps data for geographic applications on the web
Yu Zheng, Like Liu, Longhao Wang et al. · 2008 · 606 citations
Computing, Geography, Geoinformatics +17
José Ignacio Orlando, Elena Prokofyeva, Matthew B. Blaschko · IEEE Transactions on Biomedical Engineering · 2016 · 514 citations · Full text
1–5 of 277
1K
University of Massachusetts Amherst, United States
5 publications · 1K citations
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407

Chinese Academy of Sciences
Beijing, China
10 h-index
25 publications
973 citations
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
Wuhan, China
5 h-index
13 publications
1.3K citations

Xidian University
Xi'an, China
5 h-index
23 publications
2.3K citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
5 h-index
12 publications
431 citations
The University of Tokyo
Tokyo, Japan
5 h-index
19 publications
225 citations
KU Leuven
Leuven, Belgium
5 h-index
11 publications
946 citations
ETH Zurich
Zurich, Switzerland
5 h-index
11 publications
391 citations
Institute of Automation
Beijing, China
5 h-index
8 publications
158 citations
1–8 of 407
12 publications · 826 citations
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Repository · 14 publications · 390 citations
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10 publications · 1K citations
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8 publications · 281 citations
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5 publications · 216 citations
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