We propose a fast approach to 3?D object detection and pose estimation that owes its robustness to a training phase during which the target object slowly moves with respect to the ...
3D object detection and importance regression/ranking are at the core for semantically interpreting 3D medical images of computer aided diagnosis (CAD). In this paper, we propose ...
Le Lu, Jinbo Bi, Matthias Wolf, Marcos Salganicoff
This paper presents a robust object tracking method via a spatial bias appearance model learned dynamically in video. Motivated by the attention shifting among local regions of a ...
We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudiment...
This paper addresses online learning of reference object distribution in the context of two hybrid tracking schemes that combine the mean shift with local point feature correspond...