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» Using Maximum Entropy for Automatic Image Annotation
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NIPS
2001
14 years 11 months ago
Categorization by Learning and Combining Object Parts
We describe an algorithm for automatically learning discriminative components of objects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds ...
Bernd Heisele, Thomas Serre, Massimiliano Pontil, ...
143
Voted
CVPR
2009
IEEE
1216views Computer Vision» more  CVPR 2009»
16 years 4 months ago
Marked Point Processes for Crowd Counting
A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placem...
Robert T. Collins, Weina Ge

Publication
1851views
16 years 10 months ago
Cerebrovascular Segmentation from TOF Using Stochastic Models
In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the d...
M. Sabry Hassouna, Aly A. Farag, Stephen Hushek, T...
86
Voted
IPMI
2009
Springer
15 years 10 months ago
Estimating Uncertainty in Brain Region Delineations
This paper presents a method for estimating uncertainty in MRI-based brain region delineations provided by fully-automated segmentation methods. In large data sets, the uncertainty...
Karl R. Beutner, Gautam Prasad, Evan Fletcher, Cha...
BMCBI
2010
153views more  BMCBI 2010»
14 years 9 months ago
Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data
Background: Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone ...
Benedikt Zacher, Pei Fen Kuan, Achim Tresch