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CVPR
2008
IEEE
16 years 6 months ago
Discriminative modeling by Boosting on Multilevel Aggregates
This paper presents a new approach to discriminative modeling for classi cation and labeling. Our method, called Boosting on Multilevel Aggregates (BMA), adds a new class of hiera...
Jason J. Corso
154
Voted
ICANN
2005
Springer
15 years 10 months ago
Learning Features of Intermediate Complexity for the Recognition of Biological Motion
Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have n...
Rodrigo Sigala, Thomas Serre, Tomaso Poggio, Marti...
133
Voted
MLDM
2005
Springer
15 years 10 months ago
Unsupervised Learning of Visual Feature Hierarchies
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
Fabien Scalzo, Justus H. Piater
SCALESPACE
2001
Springer
15 years 9 months ago
A Multi-scale Feature Likelihood Map for Direct Evaluation of Object Hypotheses
This paper develops and investigates a new approach for evaluating feature based object hypotheses in a direct way. The idea is to compute a feature likelihood map (FLM), which is ...
Ivan Laptev, Tony Lindeberg
CVPR
2004
IEEE
15 years 8 months ago
Face Localization via Hierarchical CONDENSATION with Fisher Boosting Feature Selection
We formulate face localization as a Maximum A Posteriori Probability(MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribu...
Jilin Tu, ZhenQiu Zhang, Zhihong Zeng, Thomas S. H...