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» Conditional Random Fields for Multi-Camera Object Detection
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JMLR
2008
230views more  JMLR 2008»
13 years 7 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
CVPR
2012
IEEE
11 years 10 months ago
Augmenting deformable part models with irregular-shaped object patches
The performance of part-based object detectors generally degrades for highly flexible objects. The limited topological structure of models and pre-specified part shapes are two ...
Roozbeh Mottaghi
ICIP
2007
IEEE
14 years 9 months ago
Fast Detection of Independent Motion in Crowds Guided by Supervised Learning
Different from appearance-based methods, clustering feature points only by their motion coherence is an emerging category of approach to detecting and tracking individuals among c...
Yuan Li, Haizhou Ai
MICCAI
2007
Springer
14 years 8 months ago
Object Localization Based on Markov Random Fields and Symmetry Interest Points
We present an approach to detect anatomical structures by configurations of interest points, from a single example image. The representation of the configuration is based on Markov...
Branislav Micusík, Georg Langs, Horst Bisch...
IJRR
2010
137views more  IJRR 2010»
13 years 6 months ago
Multiclass Multimodal Detection and Tracking in Urban Environments
This paper presents a novel approach to detect and track pedestrians and cars based on the combined information retrieved from a camera and a laser range scanner. Laser data points...
Luciano Spinello, Rudolph Triebel, Roland Siegwart