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» Shape Alignment by Learning a Landmark-PDM Coupled Model
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CVPR
2005
IEEE
14 years 9 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 1 months 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 5 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 9 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 2 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