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...
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...
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....
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...
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...