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» Shape Alignment by Learning a Landmark-PDM Coupled Model
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CVPR
2005
IEEE
14 years 8 months ago
Joint Nonparametric Alignment for Analyzing Spatial Gene Expression Patterns in Drosophila Imaginal Discs
To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing i...
Parvez Ahammad, Cyrus L. Harmon, Ann Hammonds, Sha...
ICRA
2006
IEEE
177views Robotics» more  ICRA 2006»
14 years 13 days ago
Autonomous Shape Model Learning for Object Localization and Recognition
— Mobile robots do not adequately represent the objects in their environment; this weakness hinders a robot’s ability to utilize past experience. In this paper, we describe a s...
Joseph Modayil, Benjamin Kuipers
ACL
2010
13 years 4 months ago
Phylogenetic Grammar Induction
We present an approach to multilingual grammar induction that exploits a phylogeny-structured model of parameter drift. Our method does not require any translated texts or token-l...
Taylor Berg-Kirkpatrick, Dan Klein
ICIP
2001
IEEE
14 years 8 months ago
Use of a probabilistic shape model for non-linear registration of 3D scattered data
In this paper we address the problem of registering 3D scattered data by the mean of a statistical shape model. This model is built from a training set on which a principal compon...
Isabelle Corouge, Christian Barillot
CVPR
2009
IEEE
1216views Computer Vision» more  CVPR 2009»
15 years 1 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