We present an approach for efficiently recognizing all objects in a scene and estimating their full pose from multiple views. Our approach builds upon a state of the art single-vie...
Selective sampling, a form of active learning, reduces the cost of labeling training data by asking only for the labels of the most informative unlabeled examples. We introduce a ...
Traditional content-based image retrieval (CBIR) systems often fail to meet a user's need due to the `semantic gap' between the extracted features of the systems and the...
We propose a sequential Monte Carlo data association algorithm based on a two-level computational framework for tracking varying number of interacting objects in dynamic scene. Fi...
Data mining tasks such as supervised classification can often benefit from a large training dataset. However, in many application domains, privacy concerns can hinder the construc...