We describe a framework for learning an object classifier from a single example. This goal is achieved by emphasizing the relevant dimensions for classification using available ex...
— In the last decade, graph-cut optimization has been popular for a variety of labeling problems. Typically graph-cut methods are used to incorporate smoothness constraints on a ...
In this paper we investigate protocols for scheduling applications that consist of large numbers of identical, independent tasks on large-scale computing platforms. By imposing a ...
Barbara Kreaseck, Larry Carter, Henri Casanova, Je...
A key problem for "face in the crowd" recognition from existing surveillance cameras in public spaces (such as mass transit centres) is the issue of pose mismatches betwe...
Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns from digital data. However, discovery of such hidden patterns has sta...