Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
—The goal of this work is to determine the object correspondence between a sketched map and the scene depicted by the sketch, e.g., as represented by an occupancy grid map (OGM) ...
Background estimation and removal based on the joint use of range and color data produces superior results than can be achieved with either data source alone. This is increasingly...
Gaile G. Gordon, Trevor Darrell, Michael Harville,...
Digital video applications exploit the intrinsic structure of video sequences. In order to obtain and represent this structure for video annotation and indexing tasks, the main ini...
Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and man...