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NIPS
2008
15 years 3 months ago
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...
CVBIA
2005
Springer
15 years 8 months ago
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
Abstract. Markov Random Fields (MRFs) are a popular and wellmotivated model for many medical image processing tasks such as segmentation. Discriminative Random Fields (DRFs), a dis...
Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bi...
IROS
2009
IEEE
200views Robotics» more  IROS 2009»
15 years 9 months ago
Fast geometric point labeling using conditional random fields
— In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor – the Fast Point Feature Histogram...
Radu Bogdan Rusu, Andreas Holzbach, Nico Blodow, M...
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ICASSP
2007
IEEE
15 years 8 months ago
Context-Based Concept Fusion with Boosted Conditional Random Fields
The contextual relationships among different semantic concepts provide important information for automatic concept detection in images/videos. We propose a new context-based conce...
Wei Jiang, Shih-Fu Chang, Alexander C. Loui
JCB
2006
215views more  JCB 2006»
15 years 2 months ago
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation con...
Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, V...