This paper presents a general, trainable system for object detection in unconstrained, cluttered scenes. The system derives much of its power from a representation that describes a...
Most object recognition systems require large databases of real images for classifier training. To collect real images for this purpose is a difficult and expensive process. This ...
In this paper, we propose the design of VoroNet, an objectbased peer to peer overlay network relying on Voronoi tessellations, along with its theoretical analysis and experimental...
In recent years, many emerging database applications deal with large sets of continuously moving data objects. Since no computer system can commit continuously occurring infinitesi...
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 use boos...
Antonio Torralba, Kevin P. Murphy, William T. Free...