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BMVC
1998
13 years 9 months ago
ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Local Line Detectors
We introduce an object recognition system in which objects are represented as a sparse and spatially organized set of local (bent) line segments. The line segments correspond to b...
Norbert Krüger, Niklas Lüdtke
CVPR
2010
IEEE
1872views Computer Vision» more  CVPR 2010»
14 years 4 months ago
New Features and Insights for Pedestrian Detection
Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that...
Stefan Walk, Nikodem Majer, Konrad Schindler, Bern...
CLEF
2009
Springer
13 years 6 months ago
The University of Amsterdam's Concept Detection System at ImageCLEF 2009
Our group within the University of Amsterdam participated in the large-scale visual concept detection task of ImageCLEF 2009. Our experiments focus on increasing the robustness of...
Koen E. A. van de Sande, Theo Gevers, Arnold W. M....
PAMI
2008
175views more  PAMI 2008»
13 years 8 months ago
Discriminative Feature Co-Occurrence Selection for Object Detection
This paper describes an object detection framework that learns the discriminative co-occurrence of multiple features. Feature co-occurrences are automatically found by Sequential F...
Takeshi Mita, Toshimitsu Kaneko, Björn Stenge...
CLOR
2006
13 years 12 months ago
Object Detection and Localization Using Local and Global Features
Traditional approaches to object detection only look at local pieces of the image, whether it be within a sliding window or the regions around an interest point detector. However, ...
Kevin P. Murphy, Antonio B. Torralba, Daniel Eaton...