Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixe...
Motivated by the study of algorithmic problems in the domain of information security, in this paper, we study the complexity of a new class of computations over a collection of va...
We present a unified framework for modeling and solving invariant point pattern matching problems. Invariant features are encoded as potentials in a probabilistic graphical model....
Background: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks ...
Joshua J. Forman, Paul A. Clemons, Stuart L. Schre...
Workflows are modeled as hierarchically structured directed acyclic graphs in which vertices represent computational tasks, referred to as requests, and edges represent precedent c...
Nicolas G. Grounds, John K. Antonio, Jeffrey T. Mu...