Belief networks, hidden Markov models, and Markov random fields: A unifying view

1997 ◽  
Vol 18 (11-13) ◽  
pp. 1261-1268 ◽  
Author(s):  
Padhraic Smyth
Keyword(s):  
Hidden Markov Models ◽  
Random Fields ◽  
Markov Random Fields ◽  
Markov Models ◽  
Hidden Markov ◽  
Belief Networks ◽  
Markov Random

scholarly journals Hidden Markov Random Fields and Swarm Particles: A Winning Combination in Image Segmentation

IERI Procedia ◽  
2014 ◽  
Vol 10 ◽  
pp. 19-24 ◽  
Author(s):  
El-Hachemi Guerrout ◽  
Ramdane Mahiou ◽  
Samy Ait-Aoudia
Keyword(s):  
Image Segmentation ◽  
Random Fields ◽  
Markov Random Fields ◽  
Hidden Markov ◽  
Markov Random

AUV behavior recognition using behavior histograms, HMMs, and CRFs

Robotica ◽  
2014 ◽  
Vol 32 (2) ◽  
pp. 291-304 ◽  
Author(s):  
Michael Novitzky ◽  
Charles Pippin ◽  
Thomas R. Collins ◽  
Tucker R. Balch ◽  
Michael E. West
Keyword(s):  
Hidden Markov Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Markov Models ◽  
Hidden Markov ◽  
Task Assignment ◽  
Behavior Recognition ◽  
Sensor Information ◽  
Recognition Technique ◽  
And Task

SUMMARYThis paper focuses on behavior recognition in an underwater application as a substitute for communicating through acoustic transmissions, which can be unreliable. The importance of this work is that sensor information regarding other agents can be leveraged to perform behavior recognition, which is activity recognition of robots performing specific programmed behaviors, and task-assignment. This work illustrates the use of Behavior Histograms, Hidden Markov Models (HMMs), and Conditional Random Fields (CRFs) to perform behavior recognition. We present challenges associated with using each behavior recognition technique along with results on individually selected test trajectories, from simulated and real sonar data, and real-time recognition through a simulated mission.

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