Now showing items 1-4 of 4

    • Contextual models for object detection using boosted random fields 

      Torralba, Antonio; Murphy, Kevin P.; Freeman, William T. (2004-06-25)
      We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local ...
    • Contextual models for object detection using boosted random fields 

      Torralba, Antonio; Murphy, Kevin P.; Freeman, William T. (2004-06-25)
      We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and ...
    • Sharing visual features for multiclass and multiview object detection 

      Torralba, Antonio; Murphy, Kevin P.; Freeman, William T. (2004-04-14)
      We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and ...
    • Sharing visual features for multiclass and multiview object detection 

      Torralba, Antonio; Murphy, Kevin P.; Freeman, William T. (2004-04-14)
      We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and ...