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COMPGEOM
2011
ACM
14 years 10 months ago
Comparing distributions and shapes using the kernel distance
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phil...
PODS
2012
ACM
281views Database» more  PODS 2012»
13 years 9 months ago
Mergeable summaries
We study the mergeability of data summaries. Informally speaking, mergeability requires that, given two summaries on two data sets, there is a way to merge the two summaries into ...
Pankaj K. Agarwal, Graham Cormode, Zengfeng Huang,...
CVPR
2001
IEEE
16 years 8 months ago
Geometric Distributions for Catadioptric Sensor Design
We present a general method of catadioptric sensor design for realizing prescribed projections. Our method makes use of geometric distributions in 3-dimensional space, which are g...
R. Andrew Hicks, Ronald K. Perline
CVPR
2003
IEEE
16 years 8 months ago
Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference
12 In this paper we study the problem of shape analysis and its application in locating facial feature points on frontal faces. We propose a Bayesian inference solution based on ta...
Yi Zhou, Lie Gu, HongJiang Zhang
CVPR
2005
IEEE
16 years 8 months ago
Kernel-Based Bayesian Filtering for Object Tracking
Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, the algorithm is based on a Monte Ca...
Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. D...