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» Learning Boosted Asymmetric Classifiers for Object Detection
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CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
15 years 2 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
NIPS
2008
13 years 9 months ago
Cascaded Classification Models: Combining Models for Holistic Scene Understanding
One of the original goals of computer vision was to fully understand a natural scene. This requires solving several sub-problems simultaneously, including object detection, region...
Geremy Heitz, Stephen Gould, Ashutosh Saxena, Daph...
CVPR
2005
IEEE
13 years 9 months ago
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such ...
Bogdan Georgescu, Xiang Sean Zhou, Dorin Comaniciu...
PRIB
2010
Springer
242views Bioinformatics» more  PRIB 2010»
13 years 5 months ago
Consensus of Ambiguity: Theory and Application of Active Learning for Biomedical Image Analysis
Abstract. Supervised classifiers require manually labeled training samples to classify unlabeled objects. Active Learning (AL) can be used to selectively label only “ambiguous...
Scott Doyle, Anant Madabhushi
CVPR
2009
IEEE
1453views Computer Vision» more  CVPR 2009»
14 years 11 months ago
Learning Photometric Invariance From Diversified Color Model Ensembles
Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the i...
Jose M. Alvarez, Theo Gevers, Antonio M. Lopez