We formulate the problem of scene summarization as selecting a set of images that efficiently represents the visual content of a given scene. The ideal summary presents the most i...
Given an unstructured collection of captioned images of cluttered scenes featuring a variety of objects, our goal is to learn both the names and appearances of the objects. Only a...
Michael Jamieson, Afsaneh Fazly, Sven J. Dickinson...
We present a framework for extracting image contours based on geometric and structural consistency among edge element locations and orientations. The paper presents two contributi...
Automatically discovering common visual patterns from a collection of images is an interesting but yet challenging task, in part because it is computationally prohibiting. Althoug...
We present a novel approach to automatically find spatial configurations of local features occurring frequently on instances of a given object class, and rarely on the background....
Till Quack, Vittorio Ferrari, Bastian Leibe, Luc J...
In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is a...
In the task of visual object categorization, semantic context can play the very important role of reducing ambiguity in objects' visual appearance. In this work we propose to...
Andrew Rabinovich, Andrea Vedaldi, Carolina Galleg...
Large-scale 3D reconstruction has recently received much attention from the computer vision community. Bundle adjustment is a key component of 3D reconstruction problems. However,...
Closing the semantic gap in content based image retrieval (CBIR) basically requires the knowledge of the user's intention which is usually translated into a sequence of quest...