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» Learning Boosted Asymmetric Classifiers for Object Detection
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DAGM
2010
Springer
13 years 8 months ago
On-Line Multi-view Forests for Tracking
Abstract. A successful approach to tracking is to on-line learn discriminative classifiers for the target objects. Although these trackingby-detection approaches are usually fast a...
Christian Leistner, Martin Godec, Amir Saffari, Ho...
TSP
2010
13 years 2 months ago
Learning graphical models for hypothesis testing and classification
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
CVPR
2009
IEEE
15 years 2 months ago
Granularity-tunable Gradients Partition (GGP) Descriptors for Human Detection
This paper proposes a novel descriptor, granularitytunable gradients partition (GGP), for human detection. The concept granularity is used to define the spatial and angular unce...
Yazhou Liu (Harbin Institute of Technology), Shigu...
RAS
2010
167views more  RAS 2010»
13 years 5 months ago
Data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an ad...
Grzegorz Cielniak, Tom Duckett, Achim J. Lilientha...
ICCV
2007
IEEE
14 years 9 months ago
Learning to Find Object Boundaries Using Motion Cues
While great strides have been made in detecting and localizing specific objects in natural images, the bottom-up segmentation of unknown, generic objects remains a difficult chall...
Andrew N. Stein, Derek Hoiem, Martial Hebert