Models for Scene Understanding – Global Energy models and a Style- Parameterized boosting algorithm (StyP-Boost) Jonathan Warrell, 1 Simon Prince, 2 Philip Torr, 1 Lubor Ladicky, 1 Chris Russell 1 1 Oxford Brookes University, 2 University College London
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Models for Scene Understanding – Global Energy models and a Style-Parameterized boosting algorithm (StyP-Boost) Jonathan Warrell, 1 Simon Prince, 2 Philip.
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Models for Scene Understanding – Global Energy models and a Style-Parameterized
boosting algorithm (StyP-Boost)
Jonathan Warrell,1
Simon Prince,2 Philip Torr,1
Lubor Ladicky,1 Chris Russell1
1Oxford Brookes University, 2University College London
Lubor Ladicky, Paul Sturgess, Chris Russell, Sunando Sengupta, Philip H.S. Torr, Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction, BMVC 2010
• Image first convolved with 17-d filter bank• Vectors are clustered, and assigned to ~150
texton indices
TextonBoost (Shotton et al ’09)
• Texture-layout features derived from textons
• Boosted classifier predicts semantic class
DenseBoost (Ladicky et al ’09)
• DenseBoost extends TextonBoost to include• HOG• ColourHOG• Structure / Motion features
• State of the art performance on• MSRC (Ladicky et al ’09)• CamVid (Sturgess et al ’09)
Paul Sturgess, Karteek Alahari, Lubor Ladicky, Philip H.S. Torr, Combining Appearance and Structure from Motion Features for Road Scene Understanding, BMVC, 2009
Lubor Ladicky, Chris Russell, Pushmeet Kohli, Philip H.S. Torr, Associative Hierarchical CRFs for Object Class Image Segmentation, ICCV, 2009.