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» Support Vector Machines for 3D Shape Processing
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CVPR
2010
IEEE
14 years 10 months ago
Robust RVM regression using sparse outlier model
Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision p...
Kaushik Mitra, Ashok Veeraraghavan, Rama Chellappa
SCHOLARPEDIA
2008
89views more  SCHOLARPEDIA 2008»
14 years 8 months ago
Support vector clustering
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
Asa Ben-Hur
CVPR
2001
IEEE
15 years 12 months ago
Component-based Face Detection
We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support ...
Bernd Heisele, Thomas Serre, Massimiliano Pontil, ...
ICRA
2010
IEEE
134views Robotics» more  ICRA 2010»
14 years 8 months ago
Ground plane identification using LIDAR in forested environments
—To operate autonomously in forested environments, unmanned ground vehicles (UGVs) must be able to identify the load-bearing surface of the terrain (i.e. the ground). This paper ...
Matt W. McDaniel, Takayuki Nishihata, Christopher ...
MASSDATA
2007
Springer
15 years 4 months ago
A General Approach to Shape Characterization for Biomedical Problems
Abstract. In this paper, we present a general approach to shape characterization and deformation analysis of 2D/3D deformable visual objects. In particular, we define a reference ...
Davide Moroni, Petra Perner, Ovidio Salvetti